4 papers
Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
Ba-Thinh Lam, Thanh-Huy Nguyen, Hoang-Thien Nguyen +5
Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributi…
UP2D: Uncertainty-aware Progressive Pseudo-label Denoising for Source-Free Domain Adaptive Medical Image Segmentation
Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Manh D. Ho +5
Medical image segmentation models face severe performance drops under domain shifts, especially when data sharing constraints prevent access to source images. We present a novel Un…
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 +5
Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample diffi…
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
Quang-Khai Bui-Tran, Minh-Toan Dinh, Thanh-Huy Nguyen +3
Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendo…