Federated learning for medical imaging radiology

作者：Muhammad Habib ur Rehman, Walter Hugo Lopez Pinaya, Parashkev Nachev, James Teo, Sebastin Ourselin, M. Jorge Cardoso · 发表于：British Journal of Radiology · 年份：2023 · DOI：10.1259/bjr.20220890 · 被引用次数：96 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Privacy-Preserving Technologies in Data、Artificial Intelligence in Healthcare and Education

Federated 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.