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 …

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that
governs human understanding of the real world. Consequently, these models do not …

Batch Normalization (BN) is widely used in {centralized} deep learning to improve
convergence and generalization. However, in {federated} learning (FL) with decentralized …

Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust
global model. In real-world applications, local clients often operate within their limited …

Standard deep learning paradigm may not be practical over real-world heterogeneous
medical data, where new disease merges over time with data acquired in a distributed …

Standard machine learning (ML) paradigms often operate within the confines of a single
controlled environment. The conventional approach involves gathering a centralized training …