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\n  \n 2026\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Multimodal graph learning for chagas disease classification.\n \n \n \n \n\n\n \n Carcedo-Rodríguez, G.; Molino-Minero-Re, E.; Perez-Gonzalez, J.; and Hevia-Montiel, N.\n\n\n \n\n\n\n Medical & Biological Engineering & Computing. July 2026.\n \n\n\n\n
\n\n\n\n \n \n \"MultimodalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{carcedo-rodriguez_multimodal_2026,\n\ttitle = {Multimodal graph learning for chagas disease classification},\n\tissn = {1741-0444},\n\turl = {https://doi.org/10.1007/s11517-026-03631-y},\n\tdoi = {10.1007/s11517-026-03631-y},\n\tlanguage = {en},\n\turldate = {2026-08-18},\n\tjournal = {Medical \\& Biological Engineering \\& Computing},\n\tauthor = {Carcedo-Rodríguez, Gabriel and Molino-Minero-Re, Erik and Perez-Gonzalez, Jorge and Hevia-Montiel, Nidiyare},\n\tmonth = jul,\n\tyear = {2026},\n\tkeywords = {Chagas disease, Graph attention network, Graph neural network, High dimensionality, Interpretability, Multimodal learning, Variational Graph Autoencoder},\n\tfile = {Full Text PDF:/home/blanca/Zotero/storage/LCRMCUTG/Carcedo-Rodríguez et al. - 2026 - Multimodal graph learning for chagas disease classification.pdf:application/pdf},\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Deep learning for multivariate time series analysis in electronic health records: a scoping review.\n \n \n \n \n\n\n \n Hernandez-Diaz, C. A.; Vazquez, B.; and Fuentes-Pineda, G.\n\n\n \n\n\n\n Evolutionary Intelligence, 19(5): 123. August 2026.\n \n\n\n\n
\n\n\n\n \n \n \"DeepPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{hernandez-diaz_deep_2026,\n\ttitle = {Deep learning for multivariate time series analysis in electronic health records: a scoping review},\n\tvolume = {19},\n\tissn = {1864-5917},\n\tshorttitle = {Deep learning for multivariate time series analysis in electronic health records},\n\turl = {https://doi.org/10.1007/s12065-026-01246-8},\n\tdoi = {10.1007/s12065-026-01246-8},\n\tabstract = {In Electronic Health Records (EHR), variables such as medications and diagnoses are recorded repeatedly over time, providing temporal information for clinical decision-making. Deep Learning (DL) has been applied to a variety of tasks using EHR time-series data, yet their characteristics pose major analytical challenges. This review examines studies that have applied DL to the analysis of multivariate time series (MTS) in EHRs, identifying prediction tasks, data types, and strategies used to address these challenges. Methods:Following PRISMA-ScR, we searched Scopus, Web of Science, PubMed, the ACM Digital Library, and IEEE Xplore, for studies published through December 2025. We included peer-reviewed English-language studies applying DL to MTS analysis of structured EHR data. Results:We included 182 articles analyzing key aspects of DL model development in MTS analysis of EHRs. The studies covered diverse tasks and data types across multiple databases, revealing common challenges addressed with shared strategies. MIMIC-III was the most commonly used database, particularly in studies focused on disease management and monitoring tasks involving laboratory results. Most studies addressed single-step classification tasks (e.g., mortality prediction), whereas relatively few addressed MTS forecasting. Attention mechanisms were the predominant interpretability approach, and post-hoc explainability methods were increasingly used. Conclusions:The task-oriented taxonomy indicates that predictive objectives influence the choice of data, model architecture, and explanation methods. Validation on public databases is essential for replicability, while external datasets are required to assess generalizability. Interpretability and explainability remain critical for clinical adoption, and clinician collaboration is essential to ensure clinical relevance.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2026-08-18},\n\tjournal = {Evolutionary Intelligence},\n\tauthor = {Hernandez-Diaz, C. A. and Vazquez, Blanca and Fuentes-Pineda, Gibran},\n\tmonth = aug,\n\tyear = {2026},\n\tkeywords = {Deep learning, Electronic health records, Multivariate time series, Scoping review, Temporal data},\n\tpages = {123},\n\tfile = {Full Text PDF:/home/blanca/Zotero/storage/AVWRDD65/Hernandez-Diaz et al. - 2026 - Deep learning for multivariate time series analysis in electronic health records a scoping review.pdf:application/pdf},\n}\n\n
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\n In Electronic Health Records (EHR), variables such as medications and diagnoses are recorded repeatedly over time, providing temporal information for clinical decision-making. Deep Learning (DL) has been applied to a variety of tasks using EHR time-series data, yet their characteristics pose major analytical challenges. This review examines studies that have applied DL to the analysis of multivariate time series (MTS) in EHRs, identifying prediction tasks, data types, and strategies used to address these challenges. Methods:Following PRISMA-ScR, we searched Scopus, Web of Science, PubMed, the ACM Digital Library, and IEEE Xplore, for studies published through December 2025. We included peer-reviewed English-language studies applying DL to MTS analysis of structured EHR data. Results:We included 182 articles analyzing key aspects of DL model development in MTS analysis of EHRs. The studies covered diverse tasks and data types across multiple databases, revealing common challenges addressed with shared strategies. MIMIC-III was the most commonly used database, particularly in studies focused on disease management and monitoring tasks involving laboratory results. Most studies addressed single-step classification tasks (e.g., mortality prediction), whereas relatively few addressed MTS forecasting. Attention mechanisms were the predominant interpretability approach, and post-hoc explainability methods were increasingly used. Conclusions:The task-oriented taxonomy indicates that predictive objectives influence the choice of data, model architecture, and explanation methods. Validation on public databases is essential for replicability, while external datasets are required to assess generalizability. Interpretability and explainability remain critical for clinical adoption, and clinician collaboration is essential to ensure clinical relevance.\n
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\n \n\n \n \n \n \n \n \n Socioecological Trade-offs in the Yucatan Peninsula: Stakeholder Perceptions of How Urban and Tourism Development Reshape Nature’s Contributions to People.\n \n \n \n \n\n\n \n Vázquez-Martínez, P.; Mendoza-González, G.; Vazquez, B.; and Esse, C.\n\n\n \n\n\n\n Applied Spatial Analysis and Policy, 19(3): 196. August 2026.\n \n\n\n\n
\n\n\n\n \n \n \"SocioecologicalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{vazquez-martinez_socioecological_2026,\n\ttitle = {Socioecological {Trade}-offs in the {Yucatan} {Peninsula}: {Stakeholder} {Perceptions} of {How} {Urban} and {Tourism} {Development} {Reshape} {Nature}’s {Contributions} to {People}},\n\tvolume = {19},\n\tissn = {1874-4621},\n\tshorttitle = {Socioecological {Trade}-offs in the {Yucatan} {Peninsula}},\n\turl = {https://doi.org/10.1007/s12061-026-09967-0},\n\tdoi = {10.1007/s12061-026-09967-0},\n\tabstract = {Tourism expansion and urban development in the Yucatan Peninsula have intensified pressures on ecosystems, altering the natural contributions to people (NCP) that sustain regional wellbeing. To better understand emerging socioecological conflicts, we applied a multi-criteria analysis to integrate and spatially represent the perspectives of key institutional stakeholders regarding: (1) the prioritization of NCP groups per ecosystem, (2) the NCP they consider most important, (3) the main ecosystem threats, and (4) the ecosystems most affected by tourism and urban development. To achieve these objectives, the following ecosystems were assessed: Coastal lagoon, Cultivated grasslands, Popal, Savanna, Tropical Forest, Tular, Coastal dunes, Halophytic-hydrophytic vegetation, Peten, Mangrove, Palm Forest, Cenotes. Regulatory contributions—such as water and air quality regulation and protection against extreme weather—were consistently identified as the most valuable across coastal ecosystems, whereas cultivated grasslands were primarily valued for their material contributions, particularly food production. Perceptions of threats and ecosystem vulnerability varied across states, reflecting distinct tourism and urbanization trajectories. Incorporating stakeholder perspectives allowed us to identify major socioecological trade-offs and land-use transformation patterns by state. Overall, current development dynamics appear to prioritize short-term socioeconomic gains at the expense of ecological functions, particularly regulatory NCP, underscoring growing concerns about the long-term integrity of the peninsula’s socioecological systems.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2026-08-18},\n\tjournal = {Applied Spatial Analysis and Policy},\n\tauthor = {Vázquez-Martínez, Paulina and Mendoza-González, Gabriela and Vazquez, Blanca and Esse, Carlos},\n\tmonth = aug,\n\tyear = {2026},\n\tkeywords = {Nature’s Contributions to People (NCP), Socioecological trade-offs, Spatial Multi-Criteria Analysis (SMCA), Stakeholder perspectives, Urban and tourism development},\n\tpages = {196},\n\tfile = {Full Text PDF:/home/blanca/Zotero/storage/SII3AI3U/Vázquez-Martínez et al. - 2026 - Socioecological Trade-offs in the Yucatan Peninsula Stakeholder Perceptions of How Urban and Touris.pdf:application/pdf},\n}\n\n
\n
\n\n\n
\n Tourism expansion and urban development in the Yucatan Peninsula have intensified pressures on ecosystems, altering the natural contributions to people (NCP) that sustain regional wellbeing. To better understand emerging socioecological conflicts, we applied a multi-criteria analysis to integrate and spatially represent the perspectives of key institutional stakeholders regarding: (1) the prioritization of NCP groups per ecosystem, (2) the NCP they consider most important, (3) the main ecosystem threats, and (4) the ecosystems most affected by tourism and urban development. To achieve these objectives, the following ecosystems were assessed: Coastal lagoon, Cultivated grasslands, Popal, Savanna, Tropical Forest, Tular, Coastal dunes, Halophytic-hydrophytic vegetation, Peten, Mangrove, Palm Forest, Cenotes. Regulatory contributions—such as water and air quality regulation and protection against extreme weather—were consistently identified as the most valuable across coastal ecosystems, whereas cultivated grasslands were primarily valued for their material contributions, particularly food production. Perceptions of threats and ecosystem vulnerability varied across states, reflecting distinct tourism and urbanization trajectories. Incorporating stakeholder perspectives allowed us to identify major socioecological trade-offs and land-use transformation patterns by state. Overall, current development dynamics appear to prioritize short-term socioeconomic gains at the expense of ecological functions, particularly regulatory NCP, underscoring growing concerns about the long-term integrity of the peninsula’s socioecological systems.\n
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\n \n\n \n \n \n \n \n \n Impact of multimodal information on the estimation of fetal growth indicators using machine learning regression models.\n \n \n \n \n\n\n \n Castellanos-Diaz, O.; Perez-Gonzalez, J.; Arambula-Cosio, F.; Gonzalez-Meza, L.; Camargo-Marin, L.; Guzman-Huerta, M.; Vazquez-Salazar, F.; Aguilera-Perez, J.; Ortega-Castillo, V.; Medina-Banuelos, V.; and Valdes-Cristerna, R.\n\n\n \n\n\n\n Biomedical Physics & Engineering Express, 12(3): 035005. April 2026.\n \n\n\n\n
\n\n\n\n \n \n \"ImpactPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{castellanos-diaz_impact_2026,\n\ttitle = {Impact of multimodal information on the estimation of fetal growth indicators using machine learning regression models},\n\tvolume = {12},\n\turl = {https://doi.org/10.1088/2057-1976/ae5caa},\n\tdoi = {10.1088/2057-1976/ae5caa},\n\tabstract = {Accurate estimation of fetal growth indicators such as birth weight, birth length, and gestational age at birth is essential for monitoring pregnancy outcomes and guiding clinical decisions. Traditional predictive models typically rely on ultrasound-based fetometric data to estimate fetal weight or length. While valuable, these models provide estimates only at the time of measurement, rather than predicting values at birth, and may overlook important clinical and sociodemographic factors that also influence fetal growth. This study aimed to evaluate whether incorporating echographic, clinical, and sociodemographic features could improve the accuracy of predicting fetal growth indicators at birth and to quantify the contribution of each variable. Data from 154 cases were collected and processed for model development (61.5\\% for training and 38.5\\% for testing), divided into three feature sets: fetometric, clinical–sociodemographic, and combined clinical, echographic, and sociodemographic data. Six regression models were developed to predict three fetal growth indicators: birth weight, birth length, and gestational age at birth. Model performances were assessed using R2, mean absolute error (MAE), and mean absolute percentage error (MAPE). The multimodal models significantly outperformed those relying only on fetometric or clinical–sociodemographic data, with the random forest achieving the best performance for birth weight R2: 0.8991; MAE: 255.08 g; MAPE: 8.46\\%, birth length R2: 0.8679; MAE: 7.21 cm; MAPE: 2.73\\%, and gestational age at birth R2: 0.8886; MAE: 1.34 d; MAPE: 2.76\\%. Feature relevance analysis revealed that variables such as maternal height, maternal weight, placenta location, and alcohol consumption played substantial roles in prediction accuracy, alongside classic fetometric measurements such as head circumference. These findings highlight the multifactorial nature of fetal growth and demonstrate that integrating clinical and sociodemographic information enhances the performance of fetal growth prediction models, ultimately supporting improved perinatal care.},\n\tnumber = {3},\n\tjournal = {Biomedical Physics \\& Engineering Express},\n\tpublisher = {IOP Publishing},\n\tauthor = {Castellanos-Diaz, Orlando and Perez-Gonzalez, Jorge and Arambula-Cosio, Fernando and Gonzalez-Meza, Laura and Camargo-Marin, Lisbeth and Guzman-Huerta, Mario and Vazquez-Salazar, Fernanda and Aguilera-Perez, Jesus and Ortega-Castillo, Veronica and Medina-Banuelos, Veronica and Valdes-Cristerna, Raquel},\n\tmonth = apr,\n\tyear = {2026},\n\tpages = {035005},\n}\n\n
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\n Accurate estimation of fetal growth indicators such as birth weight, birth length, and gestational age at birth is essential for monitoring pregnancy outcomes and guiding clinical decisions. Traditional predictive models typically rely on ultrasound-based fetometric data to estimate fetal weight or length. While valuable, these models provide estimates only at the time of measurement, rather than predicting values at birth, and may overlook important clinical and sociodemographic factors that also influence fetal growth. This study aimed to evaluate whether incorporating echographic, clinical, and sociodemographic features could improve the accuracy of predicting fetal growth indicators at birth and to quantify the contribution of each variable. Data from 154 cases were collected and processed for model development (61.5% for training and 38.5% for testing), divided into three feature sets: fetometric, clinical–sociodemographic, and combined clinical, echographic, and sociodemographic data. Six regression models were developed to predict three fetal growth indicators: birth weight, birth length, and gestational age at birth. Model performances were assessed using R2, mean absolute error (MAE), and mean absolute percentage error (MAPE). The multimodal models significantly outperformed those relying only on fetometric or clinical–sociodemographic data, with the random forest achieving the best performance for birth weight R2: 0.8991; MAE: 255.08 g; MAPE: 8.46%, birth length R2: 0.8679; MAE: 7.21 cm; MAPE: 2.73%, and gestational age at birth R2: 0.8886; MAE: 1.34 d; MAPE: 2.76%. Feature relevance analysis revealed that variables such as maternal height, maternal weight, placenta location, and alcohol consumption played substantial roles in prediction accuracy, alongside classic fetometric measurements such as head circumference. These findings highlight the multifactorial nature of fetal growth and demonstrate that integrating clinical and sociodemographic information enhances the performance of fetal growth prediction models, ultimately supporting improved perinatal care.\n
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\n \n\n \n \n \n \n \n \n Traditional and Geometric Morphometrics and Invariant Shape Descriptors of Catfish Otoliths in the Yucatán Peninsula: Tools for Species Identification and Individual Size Estimation at Maya Archaeological Sites.\n \n \n \n \n\n\n \n Solis‐Gómez, A.; Pérez‐Gonzalez, J.; Jiménez‐Cano, N.; Badillo‐Alemán, M.; Gallardo‐Torres, A.; Hevia‐Montiel, N.; Arceo‐Carranza, D.; and Chiappa‐Carrara, X.\n\n\n \n\n\n\n Archaeometry, 68(S2). May 2026.\n \n\n\n\n
\n\n\n\n \n \n \"TraditionalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{solisgomez_traditional_2026,\n\ttitle = {Traditional and {Geometric} {Morphometrics} and {Invariant} {Shape} {Descriptors} of {Catfish} {Otoliths} in the {Yucatán} {Peninsula}: {Tools} for {Species} {Identification} and {Individual} {Size} {Estimation} at {Maya} {Archaeological} {Sites}},\n\tvolume = {68},\n\tissn = {0003-813X, 1475-4754},\n\tshorttitle = {Traditional and {Geometric} {Morphometrics} and {Invariant} {Shape} {Descriptors} of {Catfish} {Otoliths} in the {Yucatán} {Peninsula}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1111/arcm.70062},\n\tdoi = {10.1111/arcm.70062},\n\tlanguage = {en},\n\tnumber = {S2},\n\turldate = {2026-08-18},\n\tjournal = {Archaeometry},\n\tauthor = {Solis‐Gómez, Ariana and Pérez‐Gonzalez, Jorge Luis and Jiménez‐Cano, Nayeli and Badillo‐Alemán, Maribel and Gallardo‐Torres, Alfredo and Hevia‐Montiel, Nidiyare and Arceo‐Carranza, Daniel and Chiappa‐Carrara, Xavier},\n\tmonth = may,\n\tyear = {2026},\n}\n\n
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\n \n\n \n \n \n \n \n Spatio-Temporal Deep Learning-Based Segmentation of Left Ventricular Wall in Murine Model Echocardiography.\n \n \n \n\n\n \n Carcedo-Rodríguez, G.; Vazquez, B.; Perez-Gonzalez, J.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In Zuñiga-Aguilar, E.; Benítez-Mata, B.; Reyes-Lagos, J. J.; Hernandez Acosta, H. Y.; Botello Arredondo, A. I.; Bayareh Mancilla, R.; Vázquez de la Rosa, J. F.; and Gutiérrez Valenzuela, C. A., editor(s), XLVIII Mexican Conference on Biomedical Engineering, pages 108–117, Cham, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{carcedo-rodriguez_spatio-temporal_2026,\n\taddress = {Cham},\n\ttitle = {Spatio-{Temporal} {Deep} {Learning}-{Based} {Segmentation} of {Left} {Ventricular} {Wall} in {Murine} {Model} {Echocardiography}},\n\tisbn = {978-3-032-13729-6},\n\tdoi = {10.1007/978-3-032-13729-6_12},\n\tlanguage = {en},\n\tbooktitle = {{XLVIII} {Mexican} {Conference} on {Biomedical} {Engineering}},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {Carcedo-Rodríguez, Gabriel and Vazquez, Blanca and Perez-Gonzalez, Jorge and Hevia-Montiel, Nidiyare},\n\teditor = {Zuñiga-Aguilar, Esmeralda and Benítez-Mata, Balam and Reyes-Lagos, Jose Javier and Hernandez Acosta, Humiko Yahaira and Botello Arredondo, Adeodato Israel and Bayareh Mancilla, Rafael and Vázquez de la Rosa, Jaime Fabian and Gutiérrez Valenzuela, Cindy Alejandra},\n\tyear = {2026},\n\tkeywords = {Automatic segmentation, Cardiac ultrasound, Chagas disease, Convolutional LSTM, Experimental murine model, ResNet, U-Net},\n\tpages = {108--117},\n}\n\n
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\n \n\n \n \n \n \n \n Deep Learning Applied to Segmentation of Ischemic Brain Infarct Lesions in Magnetic Resonance Images.\n \n \n \n\n\n \n May-Balam, V.; Carcedo-Rodríguez, G.; Perez-Gonzalez, J. L.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In Zuñiga-Aguilar, E.; Benítez-Mata, B.; Reyes-Lagos, J. J.; Hernandez Acosta, H. Y.; Botello Arredondo, A. I.; Bayareh Mancilla, R.; Vázquez de la Rosa, J. F.; and Gutiérrez Valenzuela, C. A., editor(s), XLVIII Mexican Conference on Biomedical Engineering, pages 315–324, Cham, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{may-balam_deep_2026,\n\taddress = {Cham},\n\ttitle = {Deep {Learning} {Applied} to {Segmentation} of {Ischemic} {Brain} {Infarct} {Lesions} in {Magnetic} {Resonance} {Images}},\n\tisbn = {978-3-032-13729-6},\n\tdoi = {10.1007/978-3-032-13729-6_34},\n\tlanguage = {en},\n\tbooktitle = {{XLVIII} {Mexican} {Conference} on {Biomedical} {Engineering}},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {May-Balam, V. and Carcedo-Rodríguez, G. and Perez-Gonzalez, J. Luis and Hevia-Montiel, N.},\n\teditor = {Zuñiga-Aguilar, Esmeralda and Benítez-Mata, Balam and Reyes-Lagos, Jose Javier and Hernandez Acosta, Humiko Yahaira and Botello Arredondo, Adeodato Israel and Bayareh Mancilla, Rafael and Vázquez de la Rosa, Jaime Fabian and Gutiérrez Valenzuela, Cindy Alejandra},\n\tyear = {2026},\n\tkeywords = {attentional mechanisms, cerebral infarction, deep learning, DWI, segmentation},\n\tpages = {315--324},\n}\n\n
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\n \n\n \n \n \n \n \n \n Perfil de lípidos en mujeres mexicanas con riesgo cardiovascular.\n \n \n \n \n\n\n \n Fernández-Hernández, J. P.; Hernández-González, M. A.; Vázquez, B.; Cruz-Aceves, I.; Solorio-Meza, S. E.; and Borrayo-Sánchez, G.\n\n\n \n\n\n\n Gaceta Médica de México, 162(1): 85–92. March 2026.\n \n\n\n\n
\n\n\n\n \n \n \"PerfilPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{fernandez-hernandez_perfil_2026,\n\ttitle = {Perfil de lípidos en mujeres mexicanas con riesgo cardiovascular},\n\tvolume = {162},\n\turl = {https://www.gacetamedicademexico.com/frame_esp.php?id=1151},\n\tdoi = {10.24875/GMM.25000315},\n\tnumber = {1},\n\turldate = {2026-06-05},\n\tjournal = {Gaceta Médica de México},\n\tauthor = {Fernández-Hernández, Juan P. and Hernández-González, Martha A. and Vázquez, Blanca and Cruz-Aceves, Iván and Solorio-Meza, Sergio E. and Borrayo-Sánchez, Gabriela},\n\tmonth = mar,\n\tyear = {2026},\n\tpages = {85--92},\n\tfile = {Full Text PDF:/home/blanca/Zotero/storage/6VMIHAPS/Fernández-Hernández et al. - 2026 - Perfil de lípidos en mujeres mexicanas con riesgo cardiovascular.pdf:application/pdf},\n}\n
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\n  \n 2025\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n Deep learning for Chagas disease: YOLOv8-based automatic detection of amastigote nests.\n \n \n \n\n\n \n Sosa, D.; Vázquez, B.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In 2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM), pages 1-5, 2025. \n \n\n\n\n
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@INPROCEEDINGS{11283341,\n  author={Sosa, Daniela and Vázquez, Blanca and Hevia-Montiel, Nidiyare},\n  booktitle={2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)}, \n  title={Deep learning for Chagas disease: YOLOv8-based automatic detection of amastigote nests}, \n  year={2025},\n  volume={},\n  number={},\n  pages={1-5},\n  keywords={Deep learning;Training;Computer vision;Accuracy;Sensitivity;Histopathology;Computer architecture;Object detection;Public healthcare;Diseases;Chagas disease;YOLOv8;deep learning;computer vision;object detection;biomedical imaging},\n  doi={10.1109/SIPAIM67325.2025.11283341}}\n  \n\n\n
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\n \n\n \n \n \n \n \n Deep Learning-based Echocardiography Enhancement: Reducing Speckle Noise and Improving Temporal Resolution.\n \n \n \n\n\n \n Carcedo-Rodríguez, G.; Vazquez, B.; Perez-Gonzalez, J.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In 2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM), pages 1-5, 2025. \n \n\n\n\n
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@INPROCEEDINGS{11283395,\n  author={Carcedo-Rodríguez, Gabriel and Vazquez, Blanca and Perez-Gonzalez, Jorge and Hevia-Montiel, Nidiyare},\n  booktitle={2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)}, \n  title={Deep Learning-based Echocardiography Enhancement: Reducing Speckle Noise and Improving Temporal Resolution}, \n  year={2025},\n  volume={},\n  number={},\n  pages={1-5},\n  keywords={Heart rate;Training;Interpolation;Ultrasonic imaging;Noise reduction;Pipelines;Noise;Speckle;Spatial resolution;Testing},\n  doi={10.1109/SIPAIM67325.2025.11283395}}\n  \n
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\n \n\n \n \n \n \n \n \n Discrete Neuroimaging Metrics for Identifying Structural Alterations in COVID-19-Related Brain Atrophy.\n \n \n \n \n\n\n \n Delgado-Castillo, D.; Barbará-Morales, E.; Hevia-Montiel, N.; Arámbula-Cosío, F.; and Torres Guzman, D.\n\n\n \n\n\n\n International Journal of Online and Biomedical Engineering (iJOE), 21(13): pp. 97–112. 11 2025.\n \n\n\n\n
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@article{duniel_COVID,\n    author = {Delgado-Castillo, Duniel and Barbará-Morales, Eduardo and Hevia-Montiel, Nidiyare and Arámbula-Cosío, Fernando and Torres Guzman, Didier},\n    journal = {International Journal of Online and Biomedical Engineering (iJOE)},\n    title = {Discrete Neuroimaging Metrics for Identifying Structural Alterations in COVID-19-Related Brain Atrophy},\n    year = {2025},\n    month = {11},\n    volume={21},\n    url={https://online-journals.org/index.php/i-joe/article/view/56605},\n    DOI={10.3991/ijoe.v21i13.56605}, \n    number={13},\n    pages={pp. 97–112}\n}\n\n
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\n \n\n \n \n \n \n \n \n “ShapeNet”: A Shape Regression Convolutional Neural Network Ensemble Applied to the Segmentation of the Left Ventricle in Echocardiography.\n \n \n \n \n\n\n \n Gómez, E. G.; Torres-Robles, F.; Perez-Gonzalez, J.; and Arámbula Cosío, F.\n\n\n \n\n\n\n Journal of Imaging, 11(5): 165. May 2025.\n Publisher: Multidisciplinary Digital Publishing Institute\n\n\n\n
\n\n\n\n \n \n \"“ShapeNet”:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{gomez_shapenet_2025,\n\ttitle = {“{ShapeNet}”: {A} {Shape} {Regression} {Convolutional} {Neural} {Network} {Ensemble} {Applied} to the {Segmentation} of the {Left} {Ventricle} in {Echocardiography}},\n\tvolume = {11},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {2313-433X},\n\tshorttitle = {“{ShapeNet}”},\n\turl = {https://www.mdpi.com/2313-433X/11/5/165},\n\tdoi = {10.3390/jimaging11050165},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2025-10-31},\n\tjournal = {Journal of Imaging},\n\tauthor = {Gómez, Eduardo Galicia and Torres-Robles, Fabián and Perez-Gonzalez, Jorge and Arámbula Cosío, Fernando},\n\tmonth = may,\n\tyear = {2025},\n\tnote = {Publisher: Multidisciplinary Digital Publishing Institute},\n\tpages = {165},\n}\n\n
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\n \n\n \n \n \n \n \n \n Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts.\n \n \n \n \n\n\n \n Romero–Pacheco, A.; Hevia–Montiel, N.; Vazquez, B.; Cosío, F. A.; and Perez–Gonzalez, J.\n\n\n \n\n\n\n Journal of Medical Imaging, 12(5): 054002. September 2025.\n Publisher: SPIE\n\n\n\n
\n\n\n\n \n \n \"Deep-learning-basedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{romeropacheco_deep-learning-based_2025,\n\ttitle = {Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts},\n\tvolume = {12},\n\tissn = {2329-4302, 2329-4310},\n\turl = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-12/issue-5/054002/Deep-learning-based-estimation-of-left-ventricle-myocardial-strain-from/10.1117/1.JMI.12.5.054002.full},\n\tdoi = {10.1117/1.JMI.12.5.054002},\n\tnumber = {5},\n\turldate = {2025-10-31},\n\tjournal = {Journal of Medical Imaging},\n\tauthor = {Romero–Pacheco, Alan and Hevia–Montiel, Nidiyare and Vazquez, Blanca and Cosío, Fernando Arámbula and Perez–Gonzalez, Jorge},\n\tmonth = sep,\n\tyear = {2025},\n\tnote = {Publisher: SPIE},\n\tpages = {054002},\n}\n\n\n
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\n \n\n \n \n \n \n \n \n A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.\n \n \n \n \n\n\n \n Vazquez, B.; Perez-Gonzalez, J.; and Hevia-Montiel, N.\n\n\n \n\n\n\n BioMedical Engineering OnLine, 24(1): 110. September 2025.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{vazquez_hybrid_2025,\n\ttitle = {A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of {Trypanosoma} cruzi infection},\n\tvolume = {24},\n\tissn = {1475-925X},\n\turl = {https://doi.org/10.1186/s12938-025-01446-w},\n\tdoi = {10.1186/s12938-025-01446-w},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2025-10-31},\n\tjournal = {BioMedical Engineering OnLine},\n\tauthor = {Vazquez, Blanca and Perez-Gonzalez, Jorge and Hevia-Montiel, Nidiyare},\n\tmonth = sep,\n\tyear = {2025},\n\tpages = {110},\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Weighted–VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.\n \n \n \n \n\n\n \n Vazquez, B.; Hevia-Montiel, N.; Perez-Gonzalez, J.; and Haro, P.\n\n\n \n\n\n\n PLOS ONE, 20(3): 1-30. 03 2025.\n \n\n\n\n
\n\n\n\n \n \n \"Weighted–VAE:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{vazquez2025,\n    doi = {10.1371/journal.pone.0315843},\n    author = {Vazquez, Blanca and Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge and Haro, Paulina},\n    journal = {PLOS ONE},\n    publisher = {Public Library of Science},\n    title = {Weighted–VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection},\n    year = {2025},\n    month = {03},\n    volume = {20},\n    url = {https://doi.org/10.1371/journal.pone.0315843},\n    pages = {1-30},\n    number = {3},\n}\n\n\n
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\n \n\n \n \n \n \n \n Automatic Standard Plane Detection in Fetal Ultrasound Improved by Fine-Tuning.\n \n \n \n\n\n \n Castellanos-Díaz, N. O.; Pérez-González, J.; Arámbula-Cosío, F.; Camargo-Marín, L.; Guzmán-Huerta, M.; Escalante-Ramírez, B.; Olveres-Montiel, J.; García-Ramírez, J.; Medina-Bañuelos, V.; and Valdés-Cristerna, R.\n\n\n \n\n\n\n In Martinez-Licona, F. M.; Ballarin, V. L.; Ibarra-Ramírez, E. A.; Pérez-Buitrago, S. M.; and Berriere, L. R., editor(s), X Latin American Conference on Biomedical Engineering, pages 368–377, Cham, 2025. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{castellanos-diaz_automatic_2025,\n\taddress = {Cham},\n\ttitle = {Automatic {Standard} {Plane} {Detection} in {Fetal} {Ultrasound} {Improved} by {Fine}-{Tuning}},\n\tisbn = {978-3-031-89514-2},\n\tdoi = {10.1007/978-3-031-89514-2_31},\n\tlanguage = {en},\n\tbooktitle = {X {Latin} {American} {Conference} on {Biomedical} {Engineering}},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {Castellanos-Díaz, N. Orlando and Pérez-González, Jorge and Arámbula-Cosío, Fernando and Camargo-Marín, Lisbeth and Guzmán-Huerta, Mario and Escalante-Ramírez, Boris and Olveres-Montiel, Jimena and García-Ramírez, Jesús and Medina-Bañuelos, Verónica and Valdés-Cristerna, Raquel},\n\teditor = {Martinez-Licona, Fabiola M. and Ballarin, Virginia L. and Ibarra-Ramírez, Ernesto A. and Pérez-Buitrago, Sandra M. and Berriere, Luis R.},\n\tyear = {2025},\n\tkeywords = {Convolutional Neural Networks, Echography, Machine Learning, Standard Plane},\n\tpages = {368--377},\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.\n \n \n \n \n\n\n \n Vazquez, B.; Rojas-García, M.; Rodríguez-Esquivel, J. I.; Marquez-Acosta, J.; Aranda-Flores, C. E.; Cetina-Pérez, L. d. C.; Soto-López, S.; Estévez-García, J. A.; Bahena-Román, M.; Madrid-Marina, V.; and Torres-Poveda, K.\n\n\n \n\n\n\n Diagnostics, 15(12): 1543. January 2025.\n Publisher: Multidisciplinary Digital Publishing Institute\n\n\n\n
\n\n\n\n \n \n \"MachinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{vazquez_machine_2025,\n\ttitle = {Machine and {Deep} {Learning} for the {Diagnosis}, {Prognosis}, and {Treatment} of {Cervical} {Cancer}: {A} {Scoping} {Review}},\n\tvolume = {15},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {2075-4418},\n\tshorttitle = {Machine and {Deep} {Learning} for the {Diagnosis}, {Prognosis}, and {Treatment} of {Cervical} {Cancer}},\n\turl = {https://www.mdpi.com/2075-4418/15/12/1543},\n\tdoi = {10.3390/diagnostics15121543},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2025-10-31},\n\tjournal = {Diagnostics},\n\tauthor = {Vazquez, Blanca and Rojas-García, Mariano and Rodríguez-Esquivel, Jocelyn Isabel and Marquez-Acosta, Janeth and Aranda-Flores, Carlos E. and Cetina-Pérez, Lucely del Carmen and Soto-López, Susana and Estévez-García, Jesús A. and Bahena-Román, Margarita and Madrid-Marina, Vicente and Torres-Poveda, Kirvis},\n\tmonth = jan,\n\tyear = {2025},\n\tnote = {Publisher: Multidisciplinary Digital Publishing Institute},\n\tkeywords = {deep learning, diagnosis, machine learning, prognosis, therapeutics, uterine cervical neoplasms},\n\tpages = {1543},\n}\n\n
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\n  \n 2024\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Mangrove semantic segmentation on aerial images.\n \n \n \n \n\n\n \n Arias-Aguilar, J. A.; López-Jimenez, E.; Ramírez-Cárdenas, O. D.; Herrera-Lozada, J. C.; and Hevia-Montiel, N.\n\n\n \n\n\n\n IEEE Latin America Transactions, 22(5): 379–386. Apr. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"MangrovePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{arias_2024, \n\ttitle={Mangrove semantic segmentation on aerial images}, \n\tvolume={22}, \n\turl={https://latamt.ieeer9.org/index.php/transactions/article/view/8557},  \n\tnumber={5}, \n\tjournal={IEEE Latin America Transactions}, \n\tauthor={Arias-Aguilar, Jose Anibal and López-Jimenez, Efrén and Ramírez-Cárdenas, Oscar D. and Herrera-Lozada, J. Carlos and Hevia-Montiel, Nidiyare}, \n\tyear={2024}, \n\tmonth={Apr.}, \n\tpages={379–386} \n}\n\n
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\n \n\n \n \n \n \n \n A U-Net with Statistical Shape Restrictions Applied to the Segmentation of the Left Ventricle in Echocardiographic Images.\n \n \n \n\n\n \n Galicia-Gómez, E.; Torres-Robles, F.; Pérez, J.; Escalante-Ramírez, B.; and Cosío, F. A.\n\n\n \n\n\n\n Revista Mexicana de Ingenieria Biomedica, 44(4): 140–151. 2024.\n \n\n\n\n
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@article{galicia2024u,\n  title={A U-Net with Statistical Shape Restrictions Applied to the Segmentation of the Left Ventricle in Echocardiographic Images},\n  author={Galicia-Gómez, Eduardo and Torres-Robles, Fabían and Pérez, Jorge and Escalante-Ramírez, Boris and Cosío, Fernando Arámbula},\n  journal={Revista Mexicana de Ingenieria Biomedica},\n  volume={44},\n  number={4},\n  pages={140--151},\n  year={2024}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n 3D statistical shape models for automatic segmentation of the fetal cerebellum in ultrasound images.\n \n \n \n \n\n\n \n Velásquez-Rodríguez, G. A.; Fanti-Gutiérrez, Z.; Torres, F.; Medina-Bañuelos, V.; Escalante-Ramírez, B.; Camargo Marín, L.; Guzmán Huerta, M.; and Arámbula Cosío, F.\n\n\n \n\n\n\n Signal, Image and Video Processing, 19(1): 81. December 2024.\n \n\n\n\n
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@article{velasquez-rodriguez_3d_2024,\n\ttitle = {{3D} statistical shape models for automatic segmentation of the fetal cerebellum in ultrasound images},\n\tvolume = {19},\n\tissn = {1863-1711},\n\turl = {https://doi.org/10.1007/s11760-024-03615-1},\n\tdoi = {10.1007/s11760-024-03615-1},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2026-05-15},\n\tjournal = {Signal, Image and Video Processing},\n\tauthor = {Velásquez-Rodríguez, Gustavo A.R. and Fanti-Gutiérrez, Zian and Torres, Fabián and Medina-Bañuelos, Verónica and Escalante-Ramírez, Boris and Camargo Marín, Lisbeth and Guzmán Huerta, Mario and Arámbula Cosío, Fernando},\n\tmonth = dec,\n\tyear = {2024},\n\tkeywords = {3D segmentation of the cerebellum, Point distribution models, Spherical harmonics},\n\tpages = {81}}\n\n
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\n  \n 2023\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n Real-Time 3D Ultrasound Reconstruction Using Octrees.\n \n \n \n\n\n \n Victoria, C.; Torres, F.; Garduño, E.; Cosío, F. A.; and Gastelum-Strozzi, A.\n\n\n \n\n\n\n IEEE Access, 11: 78970-78983. 2023.\n \n\n\n\n
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@ARTICLE{10194934,\n  author={Victoria, César and Torres, Fabian and Garduño, Edgar and Cosío, Fernando Arámbula and Gastelum-Strozzi, Alfonso},\n  journal={IEEE Access}, \n  title={Real-Time 3D Ultrasound Reconstruction Using Octrees}, \n  year={2023},\n  volume={11},\n  number={},\n  pages={78970-78983},\n  keywords={Three-dimensional displays;Image reconstruction;Transducers;Octrees;Ultrasonic imaging;Real-time systems;Costs;Two dimensional displays;2D and 3D ultrasound;CUDA;morton key;octree;reconstruction},\n  doi={10.1109/ACCESS.2023.3298887}}\n\n\n
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\n \n\n \n \n \n \n \n ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model.\n \n \n \n\n\n \n Haro, P.; Hevia-Montiel, N.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Tropical Medicine and Infectious Disease, 8(3): 157. 2023.\n \n\n\n\n
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@article{haro2023ecg,\n  title={ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model},\n  author={Haro, Paulina and Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge},\n  journal={Tropical Medicine and Infectious Disease},\n  volume={8},\n  number={3},\n  pages={157},\n  year={2023},\n  publisher={MDPI}\n}\n\n
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\n \n\n \n \n \n \n \n Towards breastfeeding self-efficacy and postpartum depression estimation based on analysis of free-speech interviews through natural language processing.\n \n \n \n\n\n \n Valdeolivar-Hernandez, L. I.; Quijano, M. E. F.; Echeverria-Arjonilla, J. C.; Perez-Gonzalez, J.; and Piña-Ramirez, O.\n\n\n \n\n\n\n In 18th International Symposium on Medical Information Processing and Analysis, volume 12567, pages 252–261, 2023. SPIE\n \n\n\n\n
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@inproceedings{valdeolivar2023towards,\n  title={Towards breastfeeding self-efficacy and postpartum depression estimation based on analysis of free-speech interviews through natural language processing},\n  author={Valdeolivar-Hernandez, Luz Itzel and Quijano, Maria Eugenia Flores and Echeverria-Arjonilla, Juan Carlos and Perez-Gonzalez, Jorge and Pi{\\~n}a-Ramirez, Omar},\n  booktitle={18th International Symposium on Medical Information Processing and Analysis},\n  volume={12567},\n  pages={252--261},\n  year={2023},\n  organization={SPIE}\n}\n\n
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\n \n\n \n \n \n \n \n Mathematical Modelling of Composite Phantoms for the Calibration of Ultrasound Devices.\n \n \n \n\n\n \n Bravo-Castillero, J.; Caballero-Pérez, R. O.; Haro-Álvarez, A. P.; Heredia-López, F.; Hevia-Montiel, N.; del Carmen Jorge, M.; Medina-Esquivel, R.; Molino-Minero-Re, E.; Neme-Castillo, J. A.; Pérez-González, J. L.; and others\n\n\n \n\n\n\n Journal of Applied Research and Technology, 21(2): 154–161. 2023.\n \n\n\n\n
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@article{bravo2023mathematical,\n  title={Mathematical Modelling of Composite Phantoms for the Calibration of Ultrasound Devices},\n  author={Bravo-Castillero, Juli{\\'a}n and Caballero-P{\\'e}rez, Rogelio Oscar and Haro-{\\'A}lvarez, Ana Paulina and Heredia-L{\\'o}pez, Francisco and Hevia-Montiel, Nidiyare and del Carmen Jorge, Mar{\\'\\i}a and Medina-Esquivel, Rub{\\'e}n and Molino-Minero-Re, Erik and Neme-Castillo, Jos{\\'e} Antonio and P{\\'e}rez-Gonz{\\'a}lez, Jorge Luis and others},\n  journal={Journal of Applied Research and Technology},\n  volume={21},\n  number={2},\n  pages={154--161},\n  year={2023}\n}\n\n
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\n \n\n \n \n \n \n \n Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images.\n \n \n \n\n\n \n Hevia-Montiel, N.; Haro, P.; Guillermo-Cordero, L.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Electronics, 12(19): 4144. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hevia2023deep,\n  title={Deep Learning--Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images},\n  author={Hevia-Montiel, Nidiyare and Haro, Paulina and Guillermo-Cordero, Leonardo and Perez-Gonzalez, Jorge},\n  journal={Electronics},\n  volume={12},\n  number={19},\n  pages={4144},\n  year={2023},\n  publisher={MDPI}\n}\n\n
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\n \n\n \n \n \n \n \n Estimacion del mapa de anisotropı́a fraccional y difusividad media en materia blanca utilizando transformers.\n \n \n \n\n\n \n Bandala-Alvarez, D.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Research in Computing Science, 152(8): 209–220. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{bandala2023estimacion,\n  title={Estimacion del mapa de anisotrop{\\'\\i}a fraccional y difusividad media en materia blanca utilizando transformers},\n  author={Bandala-Alvarez, Daniel and Perez-Gonzalez, Jorge},\n  journal={Research in Computing Science},\n  volume={152},\n  number={8},\n  pages={209--220},\n  year={2023},\n  publisher={Center for Computing Research of IPN}\n}\n\n
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\n \n\n \n \n \n \n \n Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.\n \n \n \n\n\n \n Camargo-Marı́n, Lisbeth; Guzmán-Huerta, M.; Piña-Ramirez, O.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Sensors, 24(1): 2. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{camargo2023multimodal,\n  title={Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning},\n  author={Camargo-Mar{\\'\\i}n, Lisbeth and Guzm{\\'a}n-Huerta, Mario and Pi{\\~n}a-Ramirez, Omar and Perez-Gonzalez, Jorge},\n  journal={Sensors},\n  volume={24},\n  number={1},\n  pages={2},\n  year={2023},\n  publisher={MDPI}\n}\n\n
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\n \n\n \n \n \n \n \n Machine learning-based classification of children affected by malnutrition using multimodal sMRI and DTI brain images.\n \n \n \n\n\n \n Garnica, D. C.; Cota-Aguiar, R.; Vaca-Palomares, I.; Fernandez-Ruiz, J.; Hevia-Montiel, N.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n In 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM), pages 1–4, 2023. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{garnica2023machine,\n  title={Machine learning-based classification of children affected by malnutrition using multimodal sMRI and DTI brain images},\n  author={Garnica, Daniel Colin and Cota-Aguiar, Ramon and Vaca-Palomares, Israel and Fernandez-Ruiz, Juan and Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge},\n  booktitle={2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM)},\n  pages={1--4},\n  year={2023},\n  organization={IEEE}\n}\n\n
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\n  \n 2022\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi.\n \n \n \n\n\n \n Hevia-Montiel, N.; Perez-Gonzalez, J.; Neme, A.; and Haro, P.\n\n\n \n\n\n\n Electronics, 11(5): 785. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hevia2022machine,\n  title={Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi},\n  author={Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge and Neme, Antonio and Haro, Paulina},\n  journal={Electronics},\n  volume={11},\n  number={5},\n  pages={785},\n  year={2022},\n  publisher={MDPI}\n}\n\n
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\n \n\n \n \n \n \n \n ARACAM: A RGB-D multi-view photogrammetry system for lower limb 3D reconstruction applications.\n \n \n \n\n\n \n Barreto, M. A; Perez-Gonzalez, J.; Herr, H. M; and Huegel, J. C\n\n\n \n\n\n\n Sensors, 22(7): 2443. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{barreto2022aracam,\n  title={ARACAM: A RGB-D multi-view photogrammetry system for lower limb 3D reconstruction applications},\n  author={Barreto, Marco A and Perez-Gonzalez, Jorge and Herr, Hugh M and Huegel, Joel C},\n  journal={Sensors},\n  volume={22},\n  number={7},\n  pages={2443},\n  year={2022},\n  publisher={MDPI}\n}\n\n
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\n \n\n \n \n \n \n \n Estimating Echocardiographic Myocardial Strain of Left Ventricle with Deep Learning.\n \n \n \n\n\n \n Romero-Pacheco, A.; Perez-Gonzalez, J.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 3891–3894, 2022. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{romero2022estimating,\n  title={Estimating Echocardiographic Myocardial Strain of Left Ventricle with Deep Learning},\n  author={Romero-Pacheco, Alan and Perez-Gonzalez, Jorge and Hevia-Montiel, Nidiyare},\n  booktitle={2022 44th Annual International Conference of the IEEE Engineering in Medicine \\& Biology Society (EMBC)},\n  pages={3891--3894},\n  year={2022},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n The use of conformal lattice metamaterials for relieving stress in lower limb sockets: A numerical and exploratory study.\n \n \n \n\n\n \n Barreto, M. A; Cuan-Urquizo, E.; Perez-Gonzalez, J.; and Huegel, J. C\n\n\n \n\n\n\n Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 236(11): 1635–1645. 2022.\n \n\n\n\n
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\n 
@article{barreto2022use,\n  title={The use of conformal lattice metamaterials for relieving stress in lower limb sockets: A numerical and exploratory study},\n  author={Barreto, Marco A and Cuan-Urquizo, Enrique and Perez-Gonzalez, Jorge and Huegel, Joel C},\n  journal={Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine},\n  volume={236},\n  number={11},\n  pages={1635--1645},\n  year={2022},\n  publisher={SAGE Publications Sage UK: London, England}\n}\n\n
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\n \n\n \n \n \n \n \n Morphological Temporal Analysis in Subjects with Alzheimer’s Disease by Brain Graph Descriptors.\n \n \n \n\n\n \n Gonzalez–Meza, L.; Siqueiros–Garcia, J.; Hevia–Montiel, N.; Reyes–Lagos, J. J.; and Perez–Gonzalez, J.\n\n\n \n\n\n\n In Congreso Nacional de Ingenierı́a Biomédica, pages 363–370, 2022. Springer International Publishing Cham\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gonzalez2022morphological,\n  title={Morphological Temporal Analysis in Subjects with Alzheimer’s Disease by Brain Graph Descriptors},\n  author={Gonzalez--Meza, Laura and Siqueiros--Garcia, Jesus and Hevia--Montiel, Nidiyare and Reyes--Lagos, Jos{\\'e} Javier and Perez--Gonzalez, Jorge},\n  booktitle={Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  pages={363--370},\n  year={2022},\n  organization={Springer International Publishing Cham}\n}\n\n
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\n \n\n \n \n \n \n \n Desired Flight Height of a Drone in Plant Species Counting by Means of Digital Image Processing: Altura de vuelo deseada de un dron en conteo de especies vegetales mediante procesamiento digital de imágenes.\n \n \n \n\n\n \n Garcia-Hernandez, M.; Meza-Espinoza, J.; Hevia-Montiel, N.; and Garcia-Garcia, E X. M.\n\n\n \n\n\n\n Tecnologı́a Educativa Revista CONAIC, 9(3): 25–29. 2022.\n \n\n\n\n
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@article{garcia2022desired,\n  title={Desired Flight Height of a Drone in Plant Species Counting by Means of Digital Image Processing: Altura de vuelo deseada de un dron en conteo de especies vegetales mediante procesamiento digital de im{\\'a}genes},\n  author={Garcia-Hernandez, Martin and Meza-Espinoza, Juan and Hevia-Montiel, Nidiyare and Garcia-Garcia, E Xio Mara},\n  journal={Tecnolog{\\'\\i}a Educativa Revista CONAIC},\n  volume={9},\n  number={3},\n  pages={25--29},\n  year={2022}\n}\n\n
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\n  \n 2021\n \n \n (19)\n \n \n
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\n \n\n \n \n \n \n \n Modelos fenomenológicos aplicados al estudio de la COVID-19 en el estado de Yucatan.\n \n \n \n\n\n \n Mesejo-Chiong, J. A.; Mecıas, A. M.; Bravo-Castillero, J.; Hevia-Montiel, N.; and Perez-Gonzalez, J. L.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
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@Misc{mesejomodelos,\n  title={Modelos fenomenológicos aplicados al estudio de la COVID-19 en el estado de Yucatan},\n  author={Mesejo-Chiong, Jose Alejandro and Mec{\\i}as, Angela Mireya-Le{\\'o}n and Bravo-Castillero, Julian and Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge Luis},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Invariant morphological descriptors from otolith shape in environment automatic classification.\n \n \n \n\n\n \n Hevia-Montiel, N.; Perez-Gonzalez, J.; Gallardo-Torres, A.; Badillo-Aleman, M.; and Chiappa-Carrara, X.\n\n\n \n\n\n\n Journal of Applied Ichthyology. 2021.\n \n\n\n\n
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@article{hevia2021invariant,\n  title={Invariant morphological descriptors from otolith shape in environment automatic classification},\n  author={Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge and Gallardo-Torres, Alfredo and Badillo-Aleman, Maribel and Chiappa-Carrara, Xavier},\n  journal={Journal of Applied Ichthyology},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers.\n \n \n \n\n\n \n Perez-Gonzalez, J.; Jiménez-Ángeles, L.; Saavedra, K. R.; Morales, E. B.; Medina-Bañuelos, V.; Initiative, A. D. N.; and others\n\n\n \n\n\n\n Physics in Medicine & Biology, 66(15): 155010. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{perez2021mild,\n  title={Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers},\n  author={Perez-Gonzalez, Jorge and Jim{\\'e}nez-{\\'A}ngeles, Luis and Saavedra, Karla Rojas and Morales, Eduardo Barbar{\\'a} and Medina-Ba{\\~n}uelos, Ver{\\'o}nica and Alzheimer’s Disease Neuroimaging Initiative and others},\n  journal={Physics in Medicine \\& Biology},\n  volume={66},\n  number={15},\n  pages={155010},\n  year={2021},\n  publisher={IOP Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Aprendizaje Computacional Para La Estimación Automática Del Peso Al Nacimiento Usando Variables Multimodales Materno-Fetales.\n \n \n \n\n\n \n Acevedo, M. M.; Paz, J S.; and Gonzélez, J P.\n\n\n \n\n\n\n In Memorias del Congreso Nacional de Ingenierı́a Biomédica, volume 8, pages 61–64, 2021. \n \n\n\n\n
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@inproceedings{acevedo2021aprendizaje,\n  title={Aprendizaje Computacional Para La Estimaci{\\'o}n Autom{\\'a}tica Del Peso Al Nacimiento Usando Variables Multimodales Materno-Fetales},\n  author={Acevedo, MG Mart{\\'\\i}nez and Paz, J S{\\'a}nchez and Gonz{\\'e}lez, J P{\\'e}rez},\n  booktitle={Memorias del Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  volume={8},\n  number={1},\n  pages={61--64},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Clasificación Automática de Sujetos con Deterioro Cognitivo Leve mediante la Caracterización Fractal 3D en Imágenes Cerebrales de Resonancia Magnética.\n \n \n \n\n\n \n Solis-Poot, J; Sanchez-Paz, J; and Perez-Gonzalez, J\n\n\n \n\n\n\n In Memorias del Congreso Nacional de Ingenierı́a Biomédica, volume 8, pages 53–56, 2021. \n \n\n\n\n
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@inproceedings{solis2021clasificacion,\n  title={Clasificaci{\\'o}n Autom{\\'a}tica de Sujetos con Deterioro Cognitivo Leve mediante la Caracterizaci{\\'o}n Fractal 3D en Im{\\'a}genes Cerebrales de Resonancia Magn{\\'e}tica},\n  author={Solis-Poot, J and Sanchez-Paz, J and Perez-Gonzalez, J},\n  booktitle={Memorias del Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  volume={8},\n  number={1},\n  pages={53--56},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Machine Learning based Classification of Local Robotic Surgical Skills in a Training Tasks Set.\n \n \n \n\n\n \n Juarez-Villalobos, L.; Hevia-Montiel, N; and Pérez-Gonzalez, J.\n\n\n \n\n\n\n In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 4596–4599, 2021. IEEE\n \n\n\n\n
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@inproceedings{juarez2021machine,\n  title={Machine Learning based Classification of Local Robotic Surgical Skills in a Training Tasks Set},\n  author={Juarez-Villalobos, Luis and Hevia-Montiel, N and P{\\'e}rez-Gonzalez, Jorge},\n  booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \\& Biology Society (EMBC)},\n  pages={4596--4599},\n  year={2021},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Convolutional Neural Networks for Chagas’ Parasite Detection in Histopathological Images.\n \n \n \n\n\n \n Sanchez-Patiño, N.; Toriz-Vazquez, A.; Hevia-Montiel, N; and Perez-Gonzalez, J\n\n\n \n\n\n\n In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2732–2735, 2021. IEEE\n \n\n\n\n
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@inproceedings{sanchez2021convolutional,\n  title={Convolutional Neural Networks for Chagas’ Parasite Detection in Histopathological Images},\n  author={Sanchez-Pati{\\~n}o, Natalia and Toriz-Vazquez, Alfonso and Hevia-Montiel, N and Perez-Gonzalez, J},\n  booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \\& Biology Society (EMBC)},\n  pages={2732--2735},\n  year={2021},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n PET-MRI analysis to identify metabolic changes during Parkinson's disease.\n \n \n \n\n\n \n Vargas, A V.; Abad, B M.; Cristerna, R V.; Arredondo, C C.; Palomeque, C A.; Rotenberg, N K.; Medina-Bañuelos, V; and Perez-Gonzalez Sr, J\n\n\n \n\n\n\n In 17th International Symposium on Medical Information Processing and Analysis, volume 12088, pages 236–242, 2021. SPIE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vargas2021pet,\n  title={PET-MRI analysis to identify metabolic changes during Parkinson's disease},\n  author={Vargas, A Valdivia and Abad, B Moreno and Cristerna, R Vald{\\'e}s and Arredondo, C Carde{\\~n}a and Palomeque, C Aguilar and Rotenberg, N Kerik and Medina-Ba{\\~n}uelos, V and Perez-Gonzalez Sr, J},\n  booktitle={17th International Symposium on Medical Information Processing and Analysis},\n  volume={12088},\n  pages={236--242},\n  year={2021},\n  organization={SPIE}\n}\n\n
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\n \n\n \n \n \n \n \n CARACTERÍSTICAS MORFOMÉTRICAS A PARTIR DE IRM PARA LA CARACTERIZACIÓNENTRE NIÑOS CON Y SIN DESNUTRICIÓN.\n \n \n \n\n\n \n Aguiar, R. D. C.; Hevia-Montiel, N.; Palomares, I. V.; Perez-Gonzalez, J.; and Ruiz, J. F.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Misc{aguiarcaracteristicas,\n  title={CARACTER{\\'I}STICAS MORFOM{\\'E}TRICAS A PARTIR DE IRM PARA LA CARACTERIZACI{\\'O}NENTRE NI{\\~N}OS CON Y SIN DESNUTRICI{\\'O}N},\n  author={Aguiar, Ram{\\'o}n Daniel Cota and Hevia-Montiel, Nidiyare and Palomares, Israel Vaca and Perez-Gonzalez, Jorge and Ruiz, Juan Fern{\\'a}ndez},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Detección automática de señas dactilológicas por aprendizaje profundo.\n \n \n \n\n\n \n Aguiar, R. D. C.; Montiel, N. H.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Revista Aristas, 8(16): 161–166. 2021.\n \n\n\n\n
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@article{aguiar2021deteccion,\n  title={Detecci{\\'o}n autom{\\'a}tica de se{\\~n}as dactilol{\\'o}gicas por aprendizaje profundo},\n  author={Aguiar, Ram{\\'o}n Daniel Cota and Montiel, Nidiyare Hevia and Perez-Gonzalez, Jorge},\n  journal={Revista Aristas},\n  volume={8},\n  number={16},\n  pages={161--166},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Detección de la frecuencia cardiaca por técnicas de balistocardiografı́a y visión computacional.\n \n \n \n\n\n \n Peña, J. U. L.; Montiel, N. H.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Revista Aristas, 8(16): 174–180. 2021.\n \n\n\n\n
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@article{pena2021deteccion,\n  title={Detecci{\\'o}n de la frecuencia cardiaca por t{\\'e}cnicas de balistocardiograf{\\'\\i}a y visi{\\'o}n computacional.},\n  author={Pe{\\~n}a, Juan Uriel Legaria and Montiel, Nidiyare Hevia and Perez-Gonzalez, Jorge},\n  journal={Revista Aristas},\n  volume={8},\n  number={16},\n  pages={174--180},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Aprendizaje Computacional para Análisis de Imágenes de Ultrasonido Médico.\n \n \n \n\n\n \n Cosı́o, F Arámbula; Robles, F T.; Velásquez-Rodrı́guez, GAR; Gómez, E G.; Escalante-Ramirez, B; Olveres, J; Perez-Gonzalez; Bañuelos, V M.; Marı́n, L Camargo; and Huerta, M G.\n\n\n \n\n\n\n Tecnologı́a e Innovación en Educación Superior, (4): 1–14. 2021.\n \n\n\n\n
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@article{cosio2021aprendizaje,\n  title={Aprendizaje Computacional para An{\\'a}lisis de Im{\\'a}genes de Ultrasonido M{\\'e}dico},\n  author={Cos{\\'\\i}o, F Ar{\\'a}mbula and Robles, F Torres and Vel{\\'a}squez-Rodr{\\'\\i}guez, GAR and G{\\'o}mez, E Galicia and Escalante-Ramirez, B and Olveres, J and Perez-Gonzalez and Ba{\\~n}uelos, V Medina and Mar{\\'\\i}n, L Camargo and Huerta, M Guzm{\\'a}n},\n  journal={Tecnolog{\\'\\i}a e Innovaci{\\'o}n en Educaci{\\'o}n Superior},\n  number={4},\n  pages={1--14},\n  year={2021},\n  publisher={Direcci{\\'o}n General de C{\\'o}mputo y de Tecnolog{\\'\\i}as de Informaci{\\'o}n y Comunicaci{\\'o}n~…}\n}\n\n
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\n \n\n \n \n \n \n \n Aplicaciones y potencial de las técnicas de diagnóstico por imagen en la investigación biomédica de la enfermedad de Chagas.\n \n \n \n\n\n \n Haro, P.; Hevia-Montiel, N.; López-Blanco, X.; Rosado-Vallado, M.; LópezValencia, G.; and Waleckx, E.\n\n\n \n\n\n\n Revista Biomédica, 32(1): 45–54. 2021.\n \n\n\n\n
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@article{haro2021aplicaciones,\n  title={Aplicaciones y potencial de las t{\\'e}cnicas de diagn{\\'o}stico por imagen en la investigaci{\\'o}n biom{\\'e}dica de la enfermedad de Chagas},\n  author={Haro, Paulina and Hevia-Montiel, Nidiyare and L{\\'o}pez-Blanco, Xenia and Rosado-Vallado, Miguel and L{\\'o}pezValencia, Gilberto and Waleckx, Etienne},\n  journal={Revista Biom{\\'e}dica},\n  volume={32},\n  number={1},\n  pages={45--54},\n  year={2021},\n  publisher={Centro de investigaciones Regionales Dr. Hideyo Noguchi Universidad Aut{\\'o}noma~…}\n}\n\n
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\n \n\n \n \n \n \n \n Usefulness and potential of diagnostic imaging techniques in Chagas disease biomedical research.\n \n \n \n\n\n \n Haro, P.; Hevia-Montiel, N.; López-Blanco, X.; Rosado-Vallado, M.; López-Valencia, G.; and Waleckx, E.\n\n\n \n\n\n\n Revista Biomédica, 32(1): 45–54. 2021.\n \n\n\n\n
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@article{haro2021usefulness,\n  title={Usefulness and potential of diagnostic imaging techniques in Chagas disease biomedical research},\n  author={Haro, Paulina and Hevia-Montiel, Nidiyare and L{\\'o}pez-Blanco, Xenia and Rosado-Vallado, Miguel and L{\\'o}pez-Valencia, Gilberto and Waleckx, Etienne},\n  journal={Revista Biom{\\'e}dica},\n  volume={32},\n  number={1},\n  pages={45--54},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Artery/Vein Classification of Retinal Vasculature based on Cellular Automata.\n \n \n \n\n\n \n Aranda-Martinez, C.; Hevia-Montiel, N.; Rauscher, F. G; and Martinez-Perez, M E.\n\n\n \n\n\n\n In 2021 Mexican International Conference on Computer Science (ENC), pages 1–8, 2021. IEEE\n \n\n\n\n
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@inproceedings{aranda2021artery,\n  title={Artery/Vein Classification of Retinal Vasculature based on Cellular Automata},\n  author={Aranda-Martinez, Carlos and Hevia-Montiel, Nidiyare and Rauscher, Franziska G and Martinez-Perez, M Elena},\n  booktitle={2021 Mexican International Conference on Computer Science (ENC)},\n  pages={1--8},\n  year={2021},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Análisis de Textura y Aprendizaje Computacional a partir de Microfotografı́a Chagásica.\n \n \n \n\n\n \n Sánchez, E. J. R.; Curmina, V.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In Memorias del Congreso Nacional de Ingenierı́a Biomédica, volume 8, 2021. \n \n\n\n\n
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@inproceedings{sanchez2021analisis,\n  title={An{\\'a}lisis de Textura y Aprendizaje Computacional a partir de Microfotograf{\\'\\i}a Chag{\\'a}sica},\n  author={S{\\'a}nchez, Eduardo Jes{\\'u}s Rosado and Curmina, V{\\'\\i}ctor Sandoval and Hevia-Montiel, Nidiyare},\n  booktitle={Memorias del Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  volume={8},\n  number={1},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Las anomalías: ¿Qué son?, ¿Dónde surgen?, ¿Cómo detectarlas?.\n \n \n \n\n\n \n Nidiyare Hevia Montiel, S. M.\n\n\n \n\n\n\n TiES Revista de Tecnología e Innovación en Educación Superior, 4(1): 1–11. 2021.\n \n\n\n\n
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@article{nidiyare2021anomalias,\n  title={Las anomalías: ¿Qué son?, ¿Dónde surgen?, ¿Cómo detectarlas?},\n  author={Nidiyare Hevia Montiel, Sergio Mota, Antonio Neme},\n  journal={TiES Revista de Tecnología e Innovación en Educación Superior},\n  volume={4},\n  number={1},\n  pages={1--11},\n  year={2021},\n  publisher={DGTIC - UNAM}\n}\n\n
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\n \n\n \n \n \n \n \n Registro de Nubes de Puntos 2D: Comparación entre Métodos Iterativos y una Red Neuronal Artificial.\n \n \n \n\n\n \n Solís, D. M.; Martinez, H. A.; Toranzo, Y. F.; Gonzalez, M. B.; Castillero, J. B.; Hevia-Montiel, N.; and Perez-Gonzalez, J.\n\n\n \n\n\n\n Revista Aristas: Ciencia Básica y Aplicada, 8(16): 301–306. 2021.\n \n\n\n\n
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@article{solis2021registro,\n  title={Registro  de  Nubes  de  Puntos  2D:  Comparación  entre  Métodos  Iterativos  y  una  Red  Neuronal  Artificial},\n  author={Solís, Dennis Mendoza and Martinez, Hector Aranda and Toranzo, Yadira Fleitas and Gonzalez, Marco Barreto and Castillero, Julián Bravo and Hevia-Montiel, Nidiyare and Perez-Gonzalez, Jorge},\n  journal={Revista Aristas: Ciencia Básica y Aplicada},\n  volume={8},\n  number={16},\n  pages={301--306},\n  year={2021},\n  publisher={Universidad Aut{\\'o}noma de Baja California}\n}\n\n
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\n \n\n \n \n \n \n \n Sistema prototipo de monitoreo subacuático automático de peces por visión estereoscópica y aprendizaje profundo.\n \n \n \n\n\n \n Aranda-Martınez, H. C.; and Hevia-Montiel, N.\n\n\n \n\n\n\n Research in Computing Science, 150(6): 77-90. 2021.\n \n\n\n\n
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@article{arandasistema,\n  title={Sistema prototipo de monitoreo subacu{\\'a}tico autom{\\'a}tico de peces por visi{\\'o}n estereosc{\\'o}pica y aprendizaje profundo},\n  author={Aranda-Mart{\\i}nez, H{\\'e}ctor Carlos and Hevia-Montiel, Nidiyare},\n  journal ={Research in Computing Science},\n  volume={150},\n  number={6},\n  pages={77-90},\n  year={2021}\n  \n}\n\n
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\n  \n 2020\n \n \n (12)\n \n \n
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\n \n\n \n \n \n \n \n Early prediction of weight at birth using support vector regression.\n \n \n \n\n\n \n Campos Trujillo, O.; Perez-Gonzalez, J.; and Medina-Bañuelos, V.\n\n\n \n\n\n\n In VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering: Proceedings of CLAIB-CNIB 2019, October 2-5, 2019, Cancún, México, pages 37–41, 2020. Springer International Publishing\n \n\n\n\n
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@inproceedings{campos2020early,\n  title={Early prediction of weight at birth using support vector regression},\n  author={Campos Trujillo, Oliver and Perez-Gonzalez, Jorge and Medina-Ba{\\~n}uelos, Ver{\\'o}nica},\n  booktitle={VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering: Proceedings of CLAIB-CNIB 2019, October 2-5, 2019, Canc{\\'u}n, M{\\'e}xico},\n  pages={37--41},\n  year={2020},\n  organization={Springer International Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Retraining random forest algorithm for lower limb prosthesis tracking using an RGB-D camera.\n \n \n \n\n\n \n Perez-Gonzalez, J.; Carillo-Bermejo, A.; Hevia-Montiel, N.; and Huegel, J. C\n\n\n \n\n\n\n In 15th International Symposium on Medical Information Processing and Analysis, volume 11330, pages 279–285, 2020. SPIE\n \n\n\n\n
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@inproceedings{perez2020retraining,\n  title={Retraining random forest algorithm for lower limb prosthesis tracking using an RGB-D camera},\n  author={Perez-Gonzalez, Jorge and Carillo-Bermejo, Angel and Hevia-Montiel, Nidiyare and Huegel, Joel C},\n  booktitle={15th International Symposium on Medical Information Processing and Analysis},\n  volume={11330},\n  pages={279--285},\n  year={2020},\n  organization={SPIE}\n}\n\n
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\n \n\n \n \n \n \n \n Slope-chain-code-based characterization of Trypanosoma cruzi in blood smear images.\n \n \n \n\n\n \n Carrillo-Bermejo, A.; Hevia-Montiel, N.; Bribiesca, E.; Haro, P.; Rosado-Vallado, M.; Gonzalez-Mejia, E.; and Perez-Gonzalez, J\n\n\n \n\n\n\n In 15th International Symposium on Medical Information Processing and Analysis, volume 11330, pages 262–271, 2020. SPIE\n \n\n\n\n
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@inproceedings{carrillo2020slope,\n  title={Slope-chain-code-based characterization of Trypanosoma cruzi in blood smear images},\n  author={Carrillo-Bermejo, Angel and Hevia-Montiel, Nidiyare and Bribiesca, Ernesto and Haro, Paulina and Rosado-Vallado, Miguel and Gonzalez-Mejia, Emilio and Perez-Gonzalez, J},\n  booktitle={15th International Symposium on Medical Information Processing and Analysis},\n  volume={11330},\n  pages={262--271},\n  year={2020},\n  organization={SPIE}\n}\n\n
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\n \n\n \n \n \n \n \n Probabilistic learning coherent point drift for 3D ultrasound fetal head registration.\n \n \n \n\n\n \n Perez–Gonzalez, J.; Arámbula Cosío, F.; Huegel, J. C; and Medina-Banuelos, V.\n\n\n \n\n\n\n Computational and Mathematical Methods in Medicine, 2020. 2020.\n \n\n\n\n
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@article{perez2020probabilistic,\n  title={Probabilistic learning coherent point drift for 3D ultrasound fetal head registration},\n  author={Perez--Gonzalez, Jorge and Arámbula Cosío, Fernando and Huegel, Joel C and Medina-Banuelos, Verónica},\n  journal={Computational and Mathematical Methods in Medicine},\n  volume={2020},\n  year={2020},\n  publisher={Hindawi}\n}\n\n
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\n \n\n \n \n \n \n \n Evaluation of brain tortuosity measurement for the automatic multimodal classification of subjects with Alzheimer’s disease.\n \n \n \n\n\n \n Barbará-Morales, E.; Pérez-González, J.; Rojas-Saavedra, K. C; and Medina-Banuelos, V.\n\n\n \n\n\n\n Computational Intelligence and Neuroscience, 2020. 2020.\n \n\n\n\n
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@article{barbara2020evaluation,\n  title={Evaluation of brain tortuosity measurement for the automatic multimodal classification of subjects with Alzheimer’s disease},\n  author={Barbará-Morales, Eduardo and Pérez-González, Jorge and Rojas-Saavedra, Karla C and Medina-Banuelos, Veronica},\n  journal={Computational Intelligence and Neuroscience},\n  volume={2020},\n  year={2020},\n  publisher={Hindawi}\n}\n\n
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\n \n\n \n \n \n \n \n Non-static object reconstruction system based on multiple RGB-D cameras.\n \n \n \n\n\n \n Barreto, M. A; Huegel, J. C; Fuentes, R. Q; and Perez-Gonzalez, J.\n\n\n \n\n\n\n In Twelfth International Conference on Machine Vision (ICMV 2019), volume 11433, pages 613–620, 2020. SPIE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{barreto2020non,\n  title={Non-static object reconstruction system based on multiple RGB-D cameras},\n  author={Barreto, Marco A and Huegel, Joel C and Fuentes, Rita Q and Perez-Gonzalez, Jorge},\n  booktitle={Twelfth International Conference on Machine Vision (ICMV 2019)},\n  volume={11433},\n  pages={613--620},\n  year={2020},\n  organization={SPIE}\n}\n\n
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\n \n\n \n \n \n \n \n Modelo SIRD simplificado con tasas dependientes del tiempo: Estudio de la evoluci on del COVID-19 en el Estado de Yucatan.\n \n \n \n\n\n \n Bravo-Castillero, J.; Pérez-González, J. L.; Hevia-Montiel, N.; and others\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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@Misc{bravomodelo,\n  title={Modelo SIRD simplificado con tasas dependientes del tiempo: Estudio de la evoluci on del COVID-19 en el Estado de Yucatan},\n  author={Bravo-Castillero, Juli{\\'a}n and P{\\'e}rez-Gonz{\\'a}lez, Jorge Luis and Hevia-Montiel, Nidiyare and others},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Deep learning spatial compounding from multiple fetal head ultrasound acquisitions.\n \n \n \n\n\n \n Perez-Gonzalez, J.; Hevia Montiel, N.; and Bañuelos, V. M.\n\n\n \n\n\n\n In Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis: First International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings 1, pages 305–314, 2020. Springer International Publishing\n \n\n\n\n
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@inproceedings{perez2020deep,\n  title={Deep learning spatial compounding from multiple fetal head ultrasound acquisitions},\n  author={Perez-Gonzalez, Jorge and Hevia Montiel, Nidiyare and Ba{\\~n}uelos, Ver{\\'o}nica Medina},\n  booktitle={Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis: First International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings 1},\n  pages={305--314},\n  year={2020},\n  organization={Springer International Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Comparación morfológica de subestructuras cerebrales en pacientes con enfermedad de parkinson y sujetos control usando imágenes de resonancia magnética.\n \n \n \n\n\n \n Abad, B M.; Vargas, A V.; Arredondo, C C.; Palomeque, C A.; Rotenberg, N K.; Hernández, H M.; Bañuelos, V M.; Cristerna, R V.; and González, J P.\n\n\n \n\n\n\n In Memorias del Congreso Nacional de Ingenierı́a Biomédica, volume 7, pages 77–84, 2020. \n \n\n\n\n
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@inproceedings{abad2020comparacion,\n  title={Comparaci{\\'o}n morfol{\\'o}gica de subestructuras cerebrales en pacientes con enfermedad de parkinson y sujetos control usando im{\\'a}genes de resonancia magn{\\'e}tica},\n  author={Abad, B Moreno and Vargas, A Valdivia and Arredondo, C Carde{\\~n}a and Palomeque, C Aguilar and Rotenberg, N Kerik and Hern{\\'a}ndez, H Mart{\\'\\i}nez and Ba{\\~n}uelos, V Medina and Cristerna, R Vald{\\'e}s and Gonz{\\'a}lez, J P{\\'e}rez},\n  booktitle={Memorias del Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  volume={7},\n  number={1},\n  pages={77--84},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Clasificación Automática de Sujetos con Alzheimer y Deterioro Cognitivo Leve Mediante la Extracción de Caracterı́sticas de Deformación Cerebral.\n \n \n \n\n\n \n Garcı́a, C Azamar; Cristerna, R V.; and González, J P.\n\n\n \n\n\n\n In Memorias del Congreso Nacional de Ingenierı́a Biomédica, volume 7, pages 109–116, 2020. \n \n\n\n\n
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@inproceedings{garcia2020clasificacion,\n  title={Clasificaci{\\'o}n Autom{\\'a}tica de Sujetos con Alzheimer y Deterioro Cognitivo Leve Mediante la Extracci{\\'o}n de Caracter{\\'\\i}sticas de Deformaci{\\'o}n Cerebral},\n  author={Garc{\\'\\i}a, C Azamar and Cristerna, R Vald{\\'e}s and Gonz{\\'a}lez, J P{\\'e}rez},\n  booktitle={Memorias del Congreso Nacional de Ingenier{\\'\\i}a Biom{\\'e}dica},\n  volume={7},\n  number={1},\n  pages={109--116},\n  year={2020}\n}\n\n
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\n \n\n \n \n \n \n \n Segmentation of Echocardiographic Images in Murine Model of Chagas Disease.\n \n \n \n\n\n \n Viana-Camara, R.; Brito-Loeza, C.; Martin-Gonzalez, A.; and Hevia-Montiel, N.\n\n\n \n\n\n\n In Intelligent Computing Systems: Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates, March 18–19, 2020, Proceedings 3, pages 132–142, 2020. Springer International Publishing\n \n\n\n\n
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@inproceedings{viana2020segmentation,\n  title={Segmentation of Echocardiographic Images in Murine Model of Chagas Disease},\n  author={Viana-Camara, Rafael and Brito-Loeza, Carlos and Martin-Gonzalez, Anabel and Hevia-Montiel, Nidiyare},\n  booktitle={Intelligent Computing Systems: Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates, March 18--19, 2020, Proceedings 3},\n  pages={132--142},\n  year={2020},\n  organization={Springer International Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n Modelado matematico de la evoluci on de covid-19 mediante funciones tipo Gompertz.\n \n \n \n\n\n \n Trevino, C; Cajas, J.; Castillero, J. B.; Hevia-Montiel, N.; Luis, J.; Pérez-González, M. d. C. J.; and Jorge, Y. C. P.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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@Misc{trevinomodelado,\n  title={Modelado matematico de la evoluci on de covid-19 mediante funciones tipo Gompertz},\n  author={Trevino, C and Cajas, JC and Castillero, Juli{\\'a}n Bravo and Hevia-Montiel, Nidiyare and Luis, Jorge and P{\\'e}rez-Gonz{\\'a}lez, Mar{\\i}a del Carmen Jorge and Jorge, Yuriria Cort{\\'e}s Poza},\n  year={2020}\n}\n\n
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\n  \n 2019\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n Automatic classification of Alzheimer’s disease based on CPD brain point cloud registration for feature extraction.\n \n \n \n\n\n \n Perez–Gonzalez, J.; Azamar, C.; and Piña–Ramirez, O.\n\n\n \n\n\n\n In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 812–815, 2019. IEEE\n \n\n\n\n
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@inproceedings{perez2019automatic,\n  title={Automatic classification of Alzheimer’s disease based on CPD brain point cloud registration for feature extraction},\n  author={Perez--Gonzalez, Jorge and Azamar, Cristian and Pi{\\~n}a--Ramirez, Omar},\n  booktitle={2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},\n  pages={812--815},\n  year={2019},\n  organization={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n Deep learning point cloud registration based on distance features.\n \n \n \n\n\n \n Perez-Gonzalez, J.; Luna-Madrigal, F.; and Piña-Ramirez, O.\n\n\n \n\n\n\n IEEE Latin America Transactions, 17(12): 2053–2060. 2019.\n \n\n\n\n
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@article{perez2019deep,\n  title={Deep learning point cloud registration based on distance features},\n  author={Perez-Gonzalez, Jorge and Luna-Madrigal, Fernando and Pi{\\~n}a-Ramirez, Omar},\n  journal={IEEE Latin America Transactions},\n  volume={17},\n  number={12},\n  pages={2053--2060},\n  year={2019},\n  publisher={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n A chain code for representing high definition contour shapes.\n \n \n \n\n\n \n Bribiesca, E.; Bribiesca-Contreras, F.; Carrillo-Bermejo, Á.; Bribiesca-Correa, G.; and Hevia-Montiel, N.\n\n\n \n\n\n\n Journal of Visual Communication and Image Representation, 61: 93–104. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{bribiesca2019chain,\n  title={A chain code for representing high definition contour shapes},\n  author={Bribiesca, Ernesto and Bribiesca-Contreras, Fernanda and Carrillo-Bermejo, {\\'A}ngel and Bribiesca-Correa, Graciela and Hevia-Montiel, Nidiyare},\n  journal={Journal of Visual Communication and Image Representation},\n  volume={61},\n  pages={93--104},\n  year={2019},\n  publisher={Academic Press}\n}\n\n
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\n \n\n \n \n \n \n \n Caracterı́sticas de textura para la clasificación de lesiones cerebrales por bosques aleatorios.\n \n \n \n\n\n \n Navarrete-Baduy, L.; Gonzalez-Mejia, E.; Carrillo-Bermejo, A.; Hevia-Montiel, N.; Hernandez-Lopez, F. J; and Gutierrez-Alvarado, R.\n\n\n \n\n\n\n In 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{navarretecaracteristicas,\n  title={Caracter{\\'\\i}sticas de textura para la clasificaci{\\'o}n de lesiones cerebrales por bosques aleatorios},\n  author={Navarrete-Baduy, Luis and Gonzalez-Mejia, Emilio and Carrillo-Bermejo, Angel and Hevia-Montiel, Nidiyare and Hernandez-Lopez, Francisco J and Gutierrez-Alvarado, Ramon},\n  year={2019},\n  journal ={In XXI Reunión de neuroimagen},\n}\n\n
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\n \n\n \n \n \n \n \n Supervised Primary Brain Tumor Classification.\n \n \n \n\n\n \n Carrillo-Bermejo, A.; Montiel, N. H.; Bribiesca, E.; Hernandez-Lopez, F. J; and Navarrete-Baduy, L.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
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@Misc{carrillosupervised,\n  title={Supervised Primary Brain Tumor Classification},\n  author={Carrillo-Bermejo, Angel and Montiel, Nidiyare Hevia and Bribiesca, Ernesto and Hernandez-Lopez, Francisco J and Navarrete-Baduy, Luis},\n  year={2019}\n}\n\n
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