6 citations · 13 across the 8 of their papers we have counts for
5 papers · 1 filter
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
Junlin Hou, Sicen Liu, Yequan Bie +4
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable…
Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition
Shu Yang, Luyang Luo, Qiong Wang +1
Existing state-of-the-art methods for surgical phase recognition either rely on the extraction of spatial-temporal features at a short-range temporal resolution or adopt the sequen…
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Yequan Bie, Luyang Luo, Zhixuan Chen +1
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention. Within this research field, s…
Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council
Luyang Luo, Xin Huang, Minghao Wang +2
Deep learning could be prone to learning shortcuts raised by dataset bias and result in inaccurate, unreliable, and unfair models, which impedes its adoption in real-world clinical…
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
Yequan Bie, Luyang Luo, Hao Chen
Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis. However, the stringent trustworthiness requirements intrinsic to the…