Federated learning for medical imaging radiology

Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890.ABSTRACTFederated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions.PMID:38011227 | PMC:PMC10546441 | DOI:10.1259/bjr.20220890

Source: The British Journal of Radiology - November 27, 2023 Category: Radiology Authors: Muhammad Habib Ur Rehman Walter Hugo Lopez Pinaya Parashkev Nachev James T Teo Sebastin Ourselin M Jorge Cardoso Source Type: research