Fairness in artificial intelligence-driven multi-organ image segmentation  

作　　者：Qing Li Yizhe Zhang Longyu Sun Mengting Sun Meng Liu Zian Wang Qi Wang Shuo Wang Chengyan Wang 

机构地区：[1]Human Phenome Institute and Shanghai Pudong Hospital,Fudan University,Shanghai,China [2]School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,Jiangsu,China [3]School of Computer Science,Fudan University,Shanghai,China [4]School of Basic Medical Sciences,Fudan University,Shanghai,China [5]Digital Medical Research Center,Fudan University,Shanghai,China [6]International Human Phenome Institute(Shanghai),Shanghai,China

出　　处：《iRADIOLOGY》2024年第6期539-556,共18页融合影像学(英文)

基　　金：Shanghai Municipal Science and Technology Major Project,Grant/Award Number:2023SHZD2X02A05;National Natural Science Foundation of China,Grant/Award Number:62331021;Shanghai Sailing Program,Grant/Award Numbers:20YF1402400,22YF1409300。

摘　　要：Fairness is an emerging consideration when assessing the segmentation per-formance of machine learning models across various demographic groups.During clinical decision-making,an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups,resulting in severe consequences for patients and society.In medical artificial intelligence(AI),the fairness of multi-organ segmentation is imperative to augment the integration of models into clinical practice.As the use of multi-organ segmentation in medical image analysis expands,it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity.However,comprehensive studies assessing the problem of fairness in multi-organ segmentation remain lacking.This study aimed to provide an overview of the fairness problem in multi-organ segmentation.We first define fairness and discuss the factors that lead to fairness problems such as individual fairness,group fairness,counterfactual fairness,and max–min fairness in multi-organ segmentation,focusing mainly on datasets and models.We then present strategies to potentially improve fairness in multi-organ segmentation.Additionally,we highlight the challenges and limita-tions of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi-organ segmentation.

关 键 词：artificial intelligence BIAS FAIRNESS medical image multi-organ segmentation 

分 类 号：TN9[电子电信—信息与通信工程]