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 …

Contrastive Language-Image Pre-training (CLIP), a straightforward yet effective pre-training
paradigm, successfully introduces semantic-rich text supervision to vision models and has …

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 …

Deep learning (DL)-based rib fracture detection has shown promise of playing an important
role in preventing mortality and improving patient outcome. Normally, developing DL-based …

Deep learning technology can effectively assist physicians in diagnosing chest radiographs.
Conventional domain adaptation methods suffer from inaccurate lesion region localization …