1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Jiaxin Zhuang, Luyang Luo, Zhixuan Chen +1
Deep-learning (DL) based methods are playing an important role in the task of abdominal organs and tumors segmentation in CT scans. However, the large requirements of annotated dat…
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images
Yanwen Li, Luyang Luo, Huangjing Lin +2
Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based al…