Thanh-Huy Nguyen — accepted AI-conference papers

2026

ALIGNING WHAT YOU SEPARATE: DENOISED PATCH MIXING FOR SOURCE-FREE DOMAIN ADAPTATION IN MEDICAL IMAGE SEGMENTATION

Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Hoang-Thien Nguyen, Ba-Thinh Lam, Nguyen Lan Vi Vu, Phat Huynh +2

ICASSP 2026poster

Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample difficulty and struggle with noisy supervision under domain shift. We present a new SFDA framework that leverages Hard Sample…

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2026

DOMAIN-INVARIANT MIXED-DOMAIN SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION WITH CLUSTERED MAXIMUM MEAN DISCREPANCY ALIGNMENT

Ba-Thinh Lam, Thanh-Huy Nguyen, Hoang-Thien Nguyen, Nguyen Lan Vi Vu, Quang-Khai Bui-Tran, Ulas Bagci +1

ICASSP 2026poster

Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributions. In practice, annotations are scarce, and images are collected from multiple scanners or centers, leading to mixed-d…

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2026

Unsupervised Multi-Scale Segmentation of 3D Subcellular World with Stable Diffusion Foundation Model

Mostofa Rafid Uddin, HM Shadman Tabib, Thanh-Huy Nguyen, Kashish Gandhi, Min Xu

CVPR 2026

We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images. To this end, we address key challenges such as lack of annotated data, large data volumes, high heterogeneity of subcellular shapes and sizes, and high inte

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