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Addressing fairness issues in deep learning-based medical image analysis: a systematic review

文献类型：期刊论文

作者Xu, Zikang1,2; Li, Jun3; Yao, Qingsong3; Li, Han1,2; Zhao, Mingyue1,2; Zhou, S. Kevin1,2,3,4
刊名NPJ DIGITAL MEDICINE
出版日期2024-10-17
卷号7期号:1页码:16
ISSN号2398-6352
DOI10.1038/s41746-024-01276-5
英文摘要Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.
资助项目National Natural Science Foundation of China (National Science Foundation of China)[62271465] ; Natural Science Foundation of China[SYG202338] ; Suzhou Basic Research Program[YKY-KF202206] ; Open Fund Project of Guangdong Academy of Medical Sciences, China
WOS研究方向Health Care Sciences & Services ; Medical Informatics
语种英语
WOS记录号WOS:001335039900001
出版者NATURE PORTFOLIO
源URL[http://119.78.100.204/handle/2XEOYT63/39491]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhou, S. Kevin
作者单位1.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei, Anhui, Peoples R China
2.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing, Peoples R China
4.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Zikang,Li, Jun,Yao, Qingsong,et al. Addressing fairness issues in deep learning-based medical image analysis: a systematic review[J]. NPJ DIGITAL MEDICINE,2024,7(1):16.
APA Xu, Zikang,Li, Jun,Yao, Qingsong,Li, Han,Zhao, Mingyue,&Zhou, S. Kevin.(2024).Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ DIGITAL MEDICINE,7(1),16.
MLA Xu, Zikang,et al."Addressing fairness issues in deep learning-based medical image analysis: a systematic review".NPJ DIGITAL MEDICINE 7.1(2024):16.

入库方式： OAI收割

来源：计算技术研究所

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