Deep learning has been a powerful tool for medical image analysis, but large amount of
high-quality labeled datasets are generally required to train deep learning models with …

Recent advancements in large-scale visual-language pre-trained models have led to
significant progress in zero-/few-shot anomaly detection within natural image domains …

Partially class-disjoint data (PCDD), a common yet under-explored data formation where
each client contributes a part of classes (instead of all classes) of samples, severely …

Self-supervised learning (SSL) as an effective paradigm of representation learning has
achieved tremendous success on various curated datasets in diverse scenarios …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …

Federated learning aims to facilitate collaborative training among multiple clients with data
heterogeneity in a privacy-preserving manner, which either generates the generalized …

Personalized federated learning (pFL) enables collaborative training among multiple clients
to enhance the capability of customized local models. In pFL, clients may have …

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy
applicable to diverse tasks without the need for online environmental interaction. Recent …

Cross-silo federated learning (FL), which benefits from relatively abundant data and rich
computing power, is drawing increasing focus due to the significant transformations that …