3 papers
cs.CV2026
EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Models
Jiayi Chen, Yasmeen George, Winston Chong +1
Deploying foundational medical Segment Anything Models (SAMs) via test-time adaptation (TTA) is challenging under large distribution shifts, where test-time supervision is often un…
cs.CL2025
SPINE: Token-Selective Test-Time Reinforcement Learning with Entropy-Band Regularization
Jianghao Wu, Yasmeen George, Jin Ye +3
Large language models (LLMs) and multimodal LLMs (MLL-Ms) excel at chain-of-thought reasoning but face distribution shift at test-time and a lack of verifiable supervision. Recent…
cs.CV2025
SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
Jianghao Wu, Yicheng Wu, Yutong Xie +7
Leveraging the Segment Anything Model (SAM) for medical image segmentation remains challenging due to its limited adaptability across diverse medical domains. Although fine-tuned v…