Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Intelligent agents are showing increasing promise for clinical decision-making in a variety of
healthcare settings. While a substantial body of work has contributed to the best strategies to …

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …

Medical images usually suffer from image degradation in clinical practice, leading to
decreased performance of deep learning-based models. To resolve this problem, most …

Learning harmful shortcuts such as spurious correlations and biases prevents deep neural
networks from learning meaningful and useful representations, thus jeopardizing the …

Shortcut learning in deep learning models occurs when unintended features are prioritized,
resulting in degenerated feature representations and reduced generalizability and …

Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics
with the highest mortality among gynecological malignancies. Accurate and early diagnosis …

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 …

Three-dimensional organ and cancer segmentation based on multi-sequence MRI is crucial
for assisting clinical diagnosis. However, current automated segmentation methods often …