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\n  \n 2026\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching.\n \n \n \n\n\n \n Nguyen, V. P.; Trinh, N. H.; Nguyen, D. M. L.; Nguyen, P. L.; and Tran, Q. L.\n\n\n \n\n\n\n In Sudre, C. H.; Hoque, M. I.; Mehta, R.; Ouyang, C.; Qin, C.; Rakic, M.; and Wells, W. M., editor(s), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, pages 134–144, Cham, 2026. Springer Nature Switzerland\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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{10.1007/978-3-032-06593-3_13,\n    author="Nguyen, Van Phi and Trinh, Ngoc Huynh and Nguyen, Duy Minh Lam and Nguyen, Phu Loc and Tran, Quoc Long",\n    editor="Sudre, Carole H. and Hoque, Mobarak I. and Mehta, Raghav and Ouyang, Cheng and Qin, Chen and Rakic, Marianne and Wells, William M.",\n    title="Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching",\n    booktitle="Uncertainty for Safe Utilization of Machine Learning in Medical Imaging",\n    year="2026",\n    publisher="Springer Nature Switzerland",\n    address="Cham",\n    pages="134--144",\n    abstract="Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models. While current diffusion-based approaches have demonstrated impressive performance in approximating the data distribution, their inherent stochastic sampling process and inability to model exact densities limit their effectiveness in accurately capturing uncertainty. In contrast, our proposed method leverages conditional flow matching, a simulation-free flow-based generative model that learns an exact density, to produce highly accurate segmentation results. By guiding the flow model on the input image and sampling multiple data points, our approach synthesizes segmentation samples whose pixel-wise variance reliably reflects the underlying data distribution. This sampling strategy captures uncertainties in regions with ambiguous boundaries, offering robust quantification that mirrors inter-annotator differences. Experimental results demonstrate that our method not only achieves competitive segmentation accuracy but also generates uncertainty maps that provide deeper insights into the reliability of the segmentation outcomes. The code for this paper is freely available at https://github.com/huynhspm/Data-Uncertainty.",\n    isbn="978-3-032-06593-3"\n}\n\n
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\n Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models. While current diffusion-based approaches have demonstrated impressive performance in approximating the data distribution, their inherent stochastic sampling process and inability to model exact densities limit their effectiveness in accurately capturing uncertainty. In contrast, our proposed method leverages conditional flow matching, a simulation-free flow-based generative model that learns an exact density, to produce highly accurate segmentation results. By guiding the flow model on the input image and sampling multiple data points, our approach synthesizes segmentation samples whose pixel-wise variance reliably reflects the underlying data distribution. This sampling strategy captures uncertainties in regions with ambiguous boundaries, offering robust quantification that mirrors inter-annotator differences. Experimental results demonstrate that our method not only achieves competitive segmentation accuracy but also generates uncertainty maps that provide deeper insights into the reliability of the segmentation outcomes. The code for this paper is freely available at https://github.com/huynhspm/Data-Uncertainty.\n
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\n  \n 2025\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n Water level forecasting at Hanoi station using transformer-based AI models.\n \n \n \n\n\n \n An, N. L.; Long, T. Q.; Huynh, T. N.; and Son, N. H.\n\n\n \n\n\n\n Journal of Hydro-Meteorology, 22: 35–44. 2025.\n \n\n\n\n
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@article{an2025water,\n    author  = {An, N. L. and Long, T. Q. and Huynh, T. N. and Son, N. H.},\n    title   = {Water level forecasting at Hanoi station using transformer-based AI models},\n    journal = {Journal of Hydro-Meteorology},\n    year    = {2025},\n    volume  = {22},\n    pages   = {35--44}\n}\n\n
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\n \n\n \n \n \n \n \n A Robust Approach to Credit Scoring with Deep Learning and Embedded Methods.\n \n \n \n\n\n \n Pham, C. X.; Trinh, H. N.; and Tran, L. Q.\n\n\n \n\n\n\n Engineering, Technology & Applied Science Research, 15(6): 29284–29291. December 2025.\n \n\n\n\n
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@article{pham2025robust,\n    author  = {Pham, C. X. and Trinh, H. N. and Tran, L. Q.},\n    title   = {A Robust Approach to Credit Scoring with Deep Learning and Embedded Methods},\n    journal = {Engineering, Technology \\& Applied Science Research},\n    volume  = {15},\n    number  = {6},\n    pages   = {29284--29291},\n    year    = {2025},\n    month   = {December},\n}\n\n
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\n \n\n \n \n \n \n \n Ứng dụng trí tuệ nhân tạo hỗ trợ chẩn đoán ung thư phổi qua hình ảnh cắt lớp vi tính lồng ngực.\n \n \n \n\n\n \n Nguyễn Thị Thu, T.; Trần Văn, L.; Trần Quốc, L.; Lương Sơn, B.; Phạm Tiến, D.; Trịnh Ngọc, H.; Vũ Đăng, L.; and Vũ Văn, G.\n\n\n \n\n\n\n Tạp chí Y học Việt Nam, 551(1). June 2025.\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
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@article{thu2025,\n  title   = {Ứng dụng trí tuệ nhân tạo hỗ trợ chẩn đoán ung thư phổi qua hình ảnh cắt lớp vi tính lồng ngực},\n  author  = {Nguyễn Thị Thu, Thảo and Trần Văn, Lượng and Trần Quốc, Long and Lương Sơn, Bá and Phạm Tiến, Du and Trịnh Ngọc, Huỳnh and Vũ Đăng, Lưu and Vũ Văn, Giáp}, \n  journal = {Tạp chí Y học Việt Nam},\n  volume  = {551},\n  number  = {1},\n  year    = {2025},\n  month   = jun,\n  doi     = {10.51298/vmj.v551i1.14499}\n}\n\n\n
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\n \n\n \n \n \n \n \n Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis.\n \n \n \n\n\n \n Huynh, T. N.; Kien, N. D.; Anh, N. H.; Hiep, D. T.; Vaneckova, M.; Uher, T.; Van Schependom, J.; Denissen, S.; Long, T. Q.; Trung, N. L.; and others\n\n\n \n\n\n\n arXiv preprint arXiv:2510.00051. 2025.\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{huynh2025latent,\n    title={Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis},\n    author={Huynh, Trinh Ngoc and Kien, Nguyen Duc and Anh, Nguyen Hai and Hiep, Dinh Tran and Vaneckova, Manuela and Uher, Tomas and Van Schependom, Jeroen and Denissen, Stijn and Long, Tran Quoc and Trung, Nguyen Linh and others},\n    journal={arXiv preprint arXiv:2510.00051},\n    year={2025}\n}\n\n
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\n \n\n \n \n \n \n \n The Privacy–Utility Trade-off in Brain MRI Synthesis: A Comparative Framework for Generative Models.\n \n \n \n\n\n \n Ninh, D. T.; Kien, N. D.; Hiep, D. T.; D’Haeseleer, B. D. M. M.; Schependom, J. V.; Long, T. Q.; Trung, N. L.; and Nagels, G.\n\n\n \n\n\n\n In 14th International Symposium on Information and Communication Technology (SOICT), Nha Trang, Vietnam, December 2025. \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{dt.ninh2025:SoICT:GAN,\n  author  = {Dam Thai Ninh and Nguyen Duc Kien and Dinh Tran Hiep and Bui Duc Manh Miguel D’Haeseleer and Jeroen Van Schependom and Tran Quoc Long and Nguyen Linh Trung and Guy Nagels},\n  booktitle    = {14th International Symposium on Information and Communication Technology (SOICT)},\n  title        = {The Privacy–Utility Trade-off in Brain {MRI} Synthesis: A Comparative Framework for Generative Models},\n  year         = {2025},\n  address      = {Nha Trang, Vietnam},\n  month        = dec,\n}
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