Pre-training in Medical Data:A Survey

查看全文 作　　者：Yixuan [1]Qiu;Feng [1]Lin;Weitong [2]Chen;Miao [1]Xu

机构地区：[1]The University of Queensland,Brisbane 4072,Australia;[2]The University of Adelaide,Adelaide 5005,Australia

出　　处：《Machine Intelligence Research》索引2023年第20卷第2期,共33页

基　　金：supported by 2021 UQ School of Information Technology and Electrical Engineering(ITEE)Research Support Funding,Cyber Research Seed Funding(No.2021-R3);the University of Adelaide(No.1531570);New Staff Research Start-up Funds(No.NS-2102). 摘　　要：Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program.There are many categories of such data,such as clinical imaging data,bio-signal data,electronic health records(EHR),and multi-modality medical data.With the development of deep neural networks in the last decade,the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods′performance in a data-limited scenario.In recent years,studies of pre-training in the medical domain have achieved significant progress.To summarize these technology advancements,this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data.In this survey,we summarize a large number of related publications and the existing benchmarking in the medical domain.Especially,the survey briefly describes how some pre-training methods are applied to or developed for medical data.From a data-driven perspective,we examine the extensive use of pre-training in many medical scenarios.Moreover,based on the summary of recent pre-training studies,we identify several challenges in this field to provide insights for future studies. 关键词：Medical data pre-training transfer learning self-supervised learning medical image data electrocardiograms(ECG)data

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Pre-training in Medical Data:A Survey

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