4 accepted papers
Foundational vision-language models (VLMs), such as CLIP, are emerging as a promising paradigm in vision tasks due to their strong generalization ability. Nevertheless, adapting them to downstream tasks remains challenging, especially in biomedical imaging, where scarce annotations, low-contrast fea
Deep learning has achieved remarkable success in automated electrocardiogram (ECG) diagnosis. However, the long-tailed distribution of real-world ECG data remains a critical challenge. Existing methods mainly operate at the signal feature level and fail to exploit semantic relationships among diagno
Federated Adversarial Learning (FAL) is a robust framework for resisting adversarial attacks on federated learning. Although some FAL studies have developed efficient algorithms, they primarily focus on convergence performance and overlook generalization. Generalization is crucial for evaluating alg…
Myocardial infarction (MI) is one of the most dangerous cardiovascular diseases, typically diagnosed using electrocardiogram (ECG). While many deep learning methods exist for MI detection, they often overlook the correlations and medical knowledge between leads of ECG, resulting in limited interpret…