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cs.CV2025
Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
Dewen Zeng, Xinrong Hu, Yu-Jen Chen +3
Weakly supervised semantic segmentation (WSSS) methods using class labels often rely on class activation maps (CAMs) to localize objects. However, traditional CAM-based methods str…
cs.CV2025
Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation
Xinrong Hu, Yiyu Shi
Collecting pixel-level labels for medical datasets can be a laborious and expensive process, and enhancing segmentation performance with a scarcity of labeled data is a crucial cha…
cs.CV2025
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
Yu-Jen Chen, Xueyang Li, Yiyu Shi +1
Out-of-distribution (OOD) detection is essential for ensuring the reliability of deep learning models in medical imaging applications. This work is motivated by the observation tha…