Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

The advent of federated learning (FL) has presented a viable solution for distributed training
in edge environment, while simultaneously ensuring the preservation of privacy. In real …

Federated learning is an emerging technology that enables the decentralised training of
machine learning-based methods for medical image analysis across multiple sites while …

The pathogenesis of cancer is complex, involving abnormalities in some genes in
organisms. Accurately identifying cancer genes is crucial for the early detection of cancer …

Tooth point cloud segmentation is a fundamental task in many orthodontic applications.
Current research mainly focuses on fully supervised learning which demands expensive …

Due to escalating privacy concerns, federated learning has been recognized as a vital
approach for training deep neural networks with decentralized medical data. In practice, it is …

In the field of intelligent healthcare, the accessibility of medical data is severely constrained
by privacy concerns, high costs, and limited patient cases, significantly hindering automated …

Using brain imaging data to predict the non-neuroimaging phenotypes at the individual level
is a fundamental goal of system neuroscience. Despite its significance, the high acquisition …

Graph Neural Networks (GNNs) have demonstrated significant application potential in
various fields. However, GNNs are still vulnerable to adversarial attacks. Numerous …