12 papers
Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +7
Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many current adaptations pair frozen…
Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?
Yang Liu, Jiyao Yang, Hongjin Zhao +10
Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for rare conditions remains largely unexplored. Existin…
Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation
Hoang-Thien Nguyen, Thanh-Huy Nguyen, Ba-Thinh Lam +6
Teacher-student frameworks have emerged as a leading approach in semi-supervised medical image segmentation, demonstrating strong performance across various tasks. However, the lea…
From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images
Vi Vu, Thanh-Huy Nguyen, Tien-Thinh Nguyen +5
Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and th…
DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu +6
The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, t…
Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation
Mingzhou Jiang, Jiaying Zhou, Junde Wu +3
Collective intelligence from multiple medical experts consistently surpasses individual expertise in clinical diagnosis, particularly for ambiguous medical image segmentation tasks…