5 papers · 1 filter
ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
Ke Zhang, Bomin Wang, Hangqi Zhou +1
Curating fully annotated datasets for medical image segmentation is labour-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annota…
Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study
Bomin Wang, Hangqi Zhou, Yibo Gao +1
Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve…
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
Yibo Gao, Hangqi Zhou, Zheyao Gao +4
The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they…
Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
Yibo Gao, Zheyao Gao, Xin Gao +3
Due to the high stakes in medical decision-making, there is a compelling demand for interpretable deep learning methods in medical image analysis. Concept Bottleneck Models (CBM) h…
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning
Bomin Wang, Xinzhe Luo, Xiahai Zhuang
Current deep learning approaches in medical image registration usually face the challenges of distribution shift and data collection, hindering real-world deployment. In contrast,…