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.
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).
