Developing a generalized segmentation model capable of simultaneously delineating
multiple organs and diseases is highly desirable. Federated learning (FL) is a key …

Using decentralized data for federated training is one promising emerging research
direction for alleviating data scarcity in the medical domain. However, in contrast to large …

Limited training data and severe class imbalance impose significant challenges to
developing clinically robust deep learning models. Federated learning (FL) addresses the …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Federated Learning (FL) obtained a lot of attention to the academic and industrial
stakeholders from the beginning of its invention. The eye-catching feature of FL is handling …

Clinically oriented deep learning algorithms, combined with large-scale medical datasets,
have significantly promoted computer-aided diagnosis. To address increasing ethical and …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …

Federated learning is increasingly being explored in the field of medical imaging to train
deep learning models on large scale datasets distributed across different data centers while …

The shortage of Radiologists is inspiring the development of Deep Learning (DL) based
solutions for detecting cardio, thoracic and pulmonary pathologies in Chest radiographs …