4 papers
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…
MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging
Yuanye Liu, Zheyao Gao, Nannan Shi +4
Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-vie…
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Zheyao Gao, Yuanye Liu, Fuping Wu +3
Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnet…