联邦学习在医学图像处理任务中的研究综述

doi:10.11896/jsjkx.231200057

Abstract

Abstract: In the medical field,due to patient privacy concerns,it is difficult to collect and label images,which brings great difficulties to the training and deployment of deep learning models.As a distributed learning framework that can effectively protect data privacy,federated learning can conduct joint modeling on the basis that participants do not share data,and technically break the data island.With these advantages,it has been widely used in many industries.Due to the high degree of compliance with the needs of medical image processing,many federated learning research works applied to medical image processing have emerged in recent years.However,most of the new methods have not been summarized and analyzed,which is not conducive to further exploration.This paper gives a brief introduction to federated learning,lists some of its applications in medical image processing,and classifies and summarizes the existing research according to the improvement direction.Finally,the problems and challenges of federated learning in medical image are discussed,and future research directions are prospected,hoping to provide some help for subsequent research.

Key words: Federated learning, Medical image, Deep learning, Image processing, Distributed learning, Privacy protection

CLC Number:

TP391

LIU Yuming, DAI Yu, CHEN Gongping. Review of Federated Learning in Medical Image Processing[J].Computer Science, 2025, 52(1): 183-193.

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