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  2023 (2)
Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI. DeSilvio, T.; Bao, L.; Seth, D.; Chirra, P.; Singh, S.; Sridharan, A.; Labbad, M.; Bingmer, K.; Jodeh, D.; Marderstein, E. L.; Paspulati, R.; Liska, D.; Friedman, K.; Krishnamurthi, S. S.; Stein, S. L.; Purysko, A. S.; and Viswanath, S. E. In Linte, C. A.; and Siewerdsen, J. H., editor(s), Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, USA, February 19-23, 2023, volume 12466, of SPIE Proceedings, 2023. SPIE
Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI [link]Paper   doi   link   bibtex  
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models. Fan, F.; Martinez, G.; DeSilvio, T.; Shin, J.; Chen, Y.; Wang, B.; Ozeki, T.; Lafarge, M. W.; Koelzer, V. H.; Barisoni, L.; Madabhushi, A.; Viswanath, S. E.; and Janowczyk, A. CoRR, abs/2307.08673. 2023.
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models [link]Paper   doi   link   bibtex  
  2022 (4)
RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment. Antunes, J. T.; Ismail, M.; Hossain, I.; Wang, Z.; Prasanna, P.; Madabhushi, A.; Tiwari, P.; and Viswanath, S. E. IEEE J. Biomed. Health Informatics, 26(6): 2627–2636. 2022.
RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment [link]Paper   doi   link   bibtex  
Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI. Sadri, A. R.; DeSilvio, T.; Chirra, P.; Purysko, A. S.; Paspulati, R.; Friedman, K.; Krishnamurthi, S. S.; Liska, D.; Stein, S. L.; and Viswanath, S. E. In Drukker, K.; and Iftekharuddin, K. M., editor(s), Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, CA, USA, February 20-24, 2022 / online, March 21-27, 2022, volume 12033, of SPIE Proceedings, 2022. SPIE
Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI [link]Paper   doi   link   bibtex  
Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI. Sadri, A. R.; DeSilvio, T.; Chirra, P.; Singh, S.; and Viswanath, S. E. In Wang, L.; Dou, Q.; Fletcher, P. T.; Speidel, S.; and Li, S., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part III, volume 13433, of Lecture Notes in Computer Science, pages 366–375, 2022. Springer
Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI [link]Paper   doi   link   bibtex  
Identifying radiomic features associated with disease activity, patient outcomes, and serum phenotypes in pediatric Crohn's disease via MRI. Chirra, P.; Muchhala, A.; Amann, K.; Krishnan, K.; Kurowski, J.; and Viswanath, S. E. In Linte, C. A.; and Siewerdsen, J. H., editor(s), Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, USA, February 20-24, 2022 / Online, March 21-27, 2022, volume 12034, of SPIE Proceedings, 2022. SPIE
Identifying radiomic features associated with disease activity, patient outcomes, and serum phenotypes in pediatric Crohn's disease via MRI [link]Paper   doi   link   bibtex  
  2021 (2)
SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach. Sadri, A. R.; Esfahani, S. A.; Chirra, P.; Antunes, J.; Giriprakash, P. P.; Leo, P.; Madabhushi, A.; and Viswanath, S. E. In de Bruijne, M.; Cattin, P. C.; Cotin, S.; Padoy, N.; Speidel, S.; Zheng, Y.; and Essert, C., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part III, volume 12903, of Lecture Notes in Computer Science, pages 445–455, 2021. Springer
SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach [link]Paper   doi   link   bibtex  
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy. Muthusivarajan, R.; Celaya, A.; Yung, J. P.; Viswanath, S.; Marcus, D. S.; Chung, C.; and Fuentes, D. CoRR, abs/2111.01093. 2021.
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy [link]Paper   link   bibtex  
  2020 (4)
Sparse Wavelet Networks. Sadri, A. R.; Celebi, M. E.; Rahnavard, N.; and Viswanath, S. E. IEEE Signal Process. Lett., 27: 111–115. 2020.
Sparse Wavelet Networks [link]Paper   doi   link   bibtex  
Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers. Selvam, A.; Antunes, J.; Bera, K.; Ofshteyn, A.; Brady, J. T.; Bingmer, K.; Friedman, K.; Stein, S. L.; Paspulati, R.; Purysko, A. S.; Kalady, M.; Madabhushi, A.; and Viswanath, S. E. In Hahn, H. K.; and Mazurowski, M. A., editor(s), Medical Imaging 2020: Computer-Aided Diagnosis, Houston, TX, USA, February 16-19, 2020, volume 11314, of SPIE Proceedings, 2020. SPIE
Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers [link]Paper   doi   link   bibtex  
Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers. Nanda, S.; Antunes, J. T.; Bera, K.; Brady, J. T.; Friedman, K.; Willis, J. E.; Paspulati, R. M.; and Viswanath, S. E. In Fei, B.; and Linte, C. A., editor(s), Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, TX, USA, February 15-20, 2020, volume 11315, of SPIE Proceedings, pages 1131530, 2020. SPIE
Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers [link]Paper   doi   link   bibtex  
MRQy: An Open-Source Tool for Quality Control of MR Imaging Data. Sadri, A. R.; Janowczyk, A.; Zou, R.; Verma, R.; Antunes, J.; Madabhushi, A.; Tiwari, P.; and Viswanath, S. E. CoRR, abs/2004.04871. 2020.
MRQy: An Open-Source Tool for Quality Control of MR Imaging Data [link]Paper   link   bibtex  
  2019 (6)
Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. Viswanath, S.; Chirra, P.; Yim, M.; Rofsky, N. M.; Purysko, A. S.; Rosen, M. A.; Bloch, B. N.; and Madabhushi, A. BMC Medical Imaging, 19(1): 22:1–22:12. 2019.
Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study [link]Paper   doi   link   bibtex  
Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI. Jimenez, C. Á.; Barrera, C.; Múnera, N.; Viswanath, S. E.; and Romero, E. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, July 23-27, 2019, pages 2695–2698, 2019. IEEE
Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI [link]Paper   doi   link   bibtex  
STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI. Antunes, J.; Wei, Z.; Jimenez, C. Á.; Romero, E.; Ismail, M.; Madabhushi, A.; Tiwari, P.; and Viswanath, S. E. In Shen, D.; Liu, T.; Peters, T. M.; Staib, L. H.; Essert, C.; Zhou, S.; Yap, P.; and Khan, A. R., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV, volume 11767, of Lecture Notes in Computer Science, pages 611–619, 2019. Springer
STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI [link]Paper   doi   link   bibtex  
Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation. Nanda, S.; Antunes, J. T.; Selvam, A.; Bera, K.; Brady, J. T.; Gollamudi, J.; Friedman, K.; Willis, J. E.; Delaney, C. P.; Paspulati, R. M.; Madabhushi, A.; and Viswanath, S. E. In Fei, B.; and Linte, C. A., editor(s), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, USA, 16-21 February 2019, volume 10951, of SPIE Proceedings, pages 109512R, 2019. SPIE
Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation [link]Paper   doi   link   bibtex  
Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation. Yim, M. C.; Wei, Z.; Antunes, J.; Sehgal, N. K. R.; Bera, K.; Brady, J. T.; Friedman, K.; Willis, J. E.; Purysko, A. S.; Paspulati, R. M.; Madabhushi, A.; and Viswanath, S. E. In Fei, B.; and Linte, C. A., editor(s), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, USA, 16-21 February 2019, volume 10951, of SPIE Proceedings, pages 109512A, 2019. SPIE
Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation [link]Paper   doi   link   bibtex  
Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI. DeSilvio, T.; Antunes, J.; Chirra, P.; Bera, K.; Gollamudi, J.; Paspulati, R. M.; Delaney, C. P.; and Viswanath, S. E. In Fei, B.; and Linte, C. A., editor(s), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, USA, 16-21 February 2019, volume 10951, of SPIE Proceedings, pages 1095134, 2019. SPIE
Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI [link]Paper   doi   link   bibtex  
  2018 (2)
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI. Chirra, P.; Leo, P.; Yim, M.; Bloch, B. N.; Rastinehad, A. R.; Purysko, A. S.; Rosen, M.; Madabhushi, A.; and Viswanath, S. In Petrick, N. A.; and Mori, K., editor(s), Medical Imaging 2018: Computer-Aided Diagnosis, Houston, Texas, USA, 10-15 February 2018, volume 10575, of SPIE Proceedings, pages 105750B, 2018. SPIE
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI [link]Paper   doi   link   bibtex  
Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease. Barbur, I.; Kurowski, J.; Bera, K.; Thawani, R.; Achkar, J.; Fiocchi, C.; Kay, M.; Gupta, R.; and Viswanath, S. In Fei, B.; and III, R. J. W., editor(s), Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, Texas, United States, 10-15 February 2018, volume 10576, of SPIE Proceedings, pages 1057615, 2018. SPIE
Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease [link]Paper   doi   link   bibtex  
  2017 (2)
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases. Viswanath, S.; Tiwari, P.; Lee, G.; and Madabhushi, A. BMC Medical Imaging, 17(1): 2:1–2:17. 2017.
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases [link]Paper   doi   link   bibtex  
RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction. Antunes, J.; Prasanna, P.; Madabhushi, A.; Tiwari, P.; and Viswanath, S. In Descoteaux, M.; Maier-Hein, L.; Franz, A. M.; Jannin, P.; Collins, D. L.; and Duchesne, S., editor(s), Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 - 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II, volume 10434, of Lecture Notes in Computer Science, pages 468–476, 2017. Springer
RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction [link]Paper   doi   link   bibtex  
  2016 (1)
Multi-modality registration via multi-scale textural and spectral embedding representations. Li, L.; Rusu, M.; Viswanath, S.; Penzias, G.; Pahwa, S.; Gollamudi, J.; and Madabhushi, A. In Styner, M. A.; and Angelini, E. D., editor(s), Medical Imaging 2016: Image Processing, San Diego, California, USA, February 27, 2016, volume 9784, of SPIE Proceedings, pages 978446, 2016. SPIE
Multi-modality registration via multi-scale textural and spectral embedding representations [link]Paper   doi   link   bibtex  
  2014 (2)
Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer. Viswanath, S.; Toth, R.; Rusu, M.; Sperling, D.; Lepor, H.; Fütterer, J. J.; and Madabhushi, A. Neurocomputing, 144: 13–23. 2014.
Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer [link]Paper   doi   link   bibtex  
Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation. Litjens, G.; Huisman, H. J.; Elliott, R.; Shih, N.; Feldman, M. D.; Viswanath, S.; Fütterer, J. J.; Bomers, J.; and Madabhushi, A. In Yaniv, Z. R.; and III, D. R. H., editor(s), Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 15-20 February 2014, volume 9036, of SPIE Proceedings, pages 90361D, 2014. SPIE
Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation [link]Paper   doi   link   bibtex  
  2013 (2)
Discriminatively weighted multi-scale Local Binary Patterns: Applications in prostate cancer diagnosis on T2W MRI. Wang, H.; Viswanath, S.; and Madabhushi, A. In 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, 7-11 April, 2013, San Francisco, CA, USA, Proceedings, pages 398–401, 2013. IEEE
Discriminatively weighted multi-scale Local Binary Patterns: Applications in prostate cancer diagnosis on T2W MRI [link]Paper   doi   link   bibtex  
Quantitative evaluation of treatment related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer. Viswanath, S.; Toth, R.; Rusu, M.; Sperling, D.; Lepor, H.; Fütterer, J. J.; and Madabhushi, A. In III, D. R. H.; and Yaniv, Z. R., editor(s), Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, Lake Buena Vista (Orlando Area), Florida, United States, 9-14 February 2013, volume 8671, of SPIE Proceedings, pages 86711F, 2013. SPIE
Quantitative evaluation of treatment related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer [link]Paper   doi   link   bibtex  
  2012 (1)
Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data. Viswanath, S.; and Madabhushi, A. BMC Bioinform., 13: 26. 2012.
Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data [link]Paper   doi   link   bibtex  
  2011 (6)
Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI. Palumbo, D.; Yee, B.; O'Dea, P.; Leedy, S.; Viswanath, S.; and Madabhushi, A. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, MA, USA, August 30 - Sept. 3, 2011, pages 5080–5083, 2011. IEEE
Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI [link]Paper   doi   link   bibtex  
Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data. Tiwari, P.; Viswanath, S.; Lee, G.; and Madabhushi, A. In Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA, pages 165–168, 2011. IEEE
Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data [link]Paper   doi   link   bibtex  
CADOnc ©: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI. Viswanath, S.; Tiwari, P.; Chappelow, J.; Toth, R.; Kurhanewicz, J.; and Madabhushi, A. In Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA, pages 2095–2098, 2011. IEEE
CADOnc ©: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI [link]Paper   doi   link   bibtex  
Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI. Viswanath, S.; Bloch, B. N.; Chappelow, J.; Patel, P.; Rofsky, N.; Lenkinski, R. E.; Genega, E.; and Madabhushi, A. In Summers, R. M.; and van Ginneken, B., editor(s), Medical Imaging 2011: Computer-Aided Diagnosis, Lake Buena Vista (Orlando), Florida, United States, 12-17 February 2011, volume 7963, of SPIE Proceedings, pages 79630U, 2011. SPIE
Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI [link]Paper   doi   link   bibtex  
Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI. Viswanath, S.; Palumbo, D.; Chappelow, J.; Patel, P.; Bloch, B. N.; Rofsky, N.; Lenkinski, R. E.; Genega, E.; and Madabhushi, A. In Summers, R. M.; and van Ginneken, B., editor(s), Medical Imaging 2011: Computer-Aided Diagnosis, Lake Buena Vista (Orlando), Florida, United States, 12-17 February 2011, volume 7963, of SPIE Proceedings, pages 79630V, 2011. SPIE
Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI [link]Paper   doi   link   bibtex  
Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate. Tiwari, P.; Viswanath, S.; Kurhanewicz, J.; and Madabhushi, A. In Madabhushi, A.; Dowling, J.; Huisman, H. J.; and Barratt, D. C., editor(s), Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions - International Workshop, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 22, 2011. Proceedings, volume 6963, of Lecture Notes in Computer Science, pages 80–91, 2011. Springer
Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate [link]Paper   doi   link   bibtex  
  2010 (1)
Computer-assisted targeted therapy (CATT) for prostate radiotherapy planning by fusion of CT and MRI. Chappelow, J.; Both, S.; Viswanath, S.; Hahn, S.; Feldman, M. D.; Rosen, M.; Tomaszewski, J.; Vapiwala, N.; Patel, P.; and Madabhushi, A. In Wong, K. H.; and Miga, M. I., editor(s), Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, San Diego, California, United States, 13-18 February 2010, volume 7625, of SPIE Proceedings, pages 76252C, 2010. SPIE
Computer-assisted targeted therapy (CATT) for prostate radiotherapy planning by fusion of CT and MRI [link]Paper   doi   link   bibtex  
  2009 (2)
Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI. Viswanath, S.; Bloch, B. N.; Rosen, M.; Chappelow, J.; Toth, R.; Rofsky, N.; Lenkinski, R. E.; Genega, E.; Kalyanpur, A.; and Madabhushi, A. In Karssemeijer, N.; and Giger, M. L., editor(s), Medical Imaging 2009: Computer-Aided Diagnosis, Lake Buena Vista (Orlando Area), Florida, United States, 7-12 February 2009, volume 7260, of SPIE Proceedings, pages 72603I, 2009. SPIE
Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI [link]Paper   doi   link   bibtex  
COLLINARUS: collection of image-derived non-linear attributes for registration using splines. Chappelow, J.; Bloch, B. N.; Rofsky, N.; Genega, E.; Lenkinski, R. E.; DeWolf, W.; Viswanath, S.; and Madabhushi, A. In Pluim, J. P. W.; and Dawant, B. M., editor(s), Medical Imaging 2009: Image Processing, Lake Buena Vista (Orlando Area), Florida, United States, 7-12 February 2009, volume 7259, of SPIE Proceedings, pages 72592N, 2009. SPIE
COLLINARUS: collection of image-derived non-linear attributes for registration using splines [link]Paper   doi   link   bibtex  
  2008 (4)
Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer. Chappelow, J.; Viswanath, S.; Monaco, J.; Rosen, M.; Tomaszewski, J.; Feldman, M. D.; and Madabhushi, A. In Giger, M. L.; and Karssemeijer, N., editor(s), Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, California, United States, 16-21 February 2008, volume 6915, of SPIE Proceedings, pages 69150V, 2008. SPIE
Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer [link]Paper   doi   link   bibtex  
A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery. Viswanath, S.; Rosen, M.; and Madabhushi, A. In Giger, M. L.; and Karssemeijer, N., editor(s), Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, California, United States, 16-21 February 2008, volume 6915, of SPIE Proceedings, pages 69150U, 2008. SPIE
A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery [link]Paper   doi   link   bibtex  
A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging. Viswanath, S.; Tiwari, P.; Rosen, M.; and Madabhushi, A. In Giger, M. L.; and Karssemeijer, N., editor(s), Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, California, United States, 16-21 February 2008, volume 6915, of SPIE Proceedings, pages 69153D, 2008. SPIE
A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging [link]Paper   doi   link   bibtex  
A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In VivoProstate DCE-MRI. Viswanath, S.; Bloch, B. N.; Genega, E.; Rofsky, N.; Lenkinski, R. E.; Chappelow, J.; Toth, R.; and Madabhushi, A. In Metaxas, D. N.; Axel, L.; Fichtinger, G.; and Székely, G., editor(s), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I, volume 5241, of Lecture Notes in Computer Science, pages 662–669, 2008. Springer
A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In VivoProstate DCE-MRI [link]Paper   doi   link   bibtex