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 …

Background Deep learning–based segmentation algorithms usually required large or multi‐
institute data sets to improve the performance and ability of generalization. However …

of a centralized model. Li et al (5) subsequently recognized the need for privacy-preserving
methods and implemented differential privacy techniques to reduce the possible risk of …

While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use
small, non-diverse patient cohorts due to data sharing and privacy issues. Federated …

Federated learning (FL) in healthcare suffers from non-identically distributed (non-IID) data,
impacting model convergence and performance. While existing solutions for the non-IID …

Although fully automated volumetric approaches for monitoring brain tumor response have
many advantages, most available deep learning models are optimized for highly curated …

Objective: Federated Learning (FL) enables collaborative model training while keeping data
locally. Currently, most FL studies in radiology are conducted in simulated environments due …

of a centralized model. Li et al (5) subsequently recognized the need for privacy-preserving
methods and implemented differential privacy techniques to reduce the possible risk of …

The application of machine learning has been successful in enhancing our day-to-day
activities by facilitating automation and enhancing decision-making in a range of industries …