Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Arizona State University（亚利桑那州立大学）；University of Rochester（罗切斯特大学）；University of Virginia（弗吉尼亚大学）；UCL（伦敦大学学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
The Boundaries of Fair AI in Medical Image Prognosis: A Causal Perspective
Department of Computer Science and Engineering, The Ohio State University（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University（生物医学信息学系，俄亥俄州立大学）；Department of Ophthalmology and Visual Sciences, The Ohio State University（眼科学与视觉科学系，俄亥俄州立大学）
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
The University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（矢量研究所）；University of Science and Technology of China（中国科学技术大学）；The Chinese University of Hong Kong（香港中文大学）
Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州研究院）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）；New York University（纽约大学）；Harvard University（哈佛大学）
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Lassonde School of Engineering, York University（约克大学拉萨onde工程学院）
Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance
Department of Data Science, Dana-Farber Cancer Institute（丹娜-法伯癌症研究所数据科学系）；AI in Medicine, Technical University of Munich（慕尼黑工业大学医学人工智能）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）；Harvard Medical School（哈佛医学院）
Estimating and Improving Fairness with Adversarial Learning
Princeton University（普林斯顿大学）；Shanghai Jiaotong University（上海交通大学）；University of Pennsylvania（宾夕法尼亚大学）；RIKEN AIP（理化学研究所先进智能项目中心）；University of Tokyo（东京大学）
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Queen Mary University of London（伦敦玛丽女王大学）；National Technical University of Athens（雅典国家技术大学）；National Infrastructures for Research and Technology（国家研究与技术基础设施机构）
Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis
Department of Computer Science, University of Rochester, NY, USA（罗切斯特大学计算机科学系）；Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA（印第安纳大学医学院生物统计学与健康数据科学系）；Department of Computer Science, University of Central Florida, FL, USA（佛罗里达州立大学计算机科学系）
MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那州立大学拉法叶分校计算机与信息学学院）；Louisiana Center for Health Innovation and College of Nursing & Health Sciences, University of Louisiana at Lafayette（路易斯安那州立大学拉法叶分校健康创新中心及护理与健康科学学院）；Massachusetts Eye and Ear, Harvard Medical School（哈佛医学院马萨诸塞眼耳医院）；Department of Computer Science, University of Central Florida（佛罗里达州立大学计算机科学系）；Department of Electrical Engineering and Computer Science, Florida Atlantic University（佛罗里达Atlantic大学电子工程与计算机科学系）
Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（集成空天地海大数据应用技术国家工程实验室）；Northwestern Polytechnical University（西北工业大学）；Huiying Medical Technology Company Ltd.（慧影医疗技术有限公司）；The School of Computer Science（计算机学院）；The University of Sydney（悉尼大学）；Department of Computer Science and Engineering（计算机科学与工程系）；The Chinese University of Hong Kong（香港中文大学）
CAT: Controllable Attribute Translation for Fair Facial Attribute Classification
USC Ming Hsieh Department of Electrical and Computer Engineering（南加州大学明谢电气工程与计算机工程系）；USC Information Sciences Institute（南加州大学信息科学研究所）；Visual Intelligence and Multimedia Analytics Laboratory（视觉智能与多媒体分析实验室）
On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes
Paderborn University（帕德博恩大学）；Technical University of Darmstadt（达姆施塔特技术大学）；University of Notre Dame（诺特丹大学）；Johannes Gutenberg University Mainz（美因茨约翰· Gutenberg大学）
