Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Massachusetts Institute of Technology（麻省理工学院）；University of California, San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；University of Washington School of Medicine（华盛顿大学医学院）
Yuzhe Yang, Yujia Liu, Xin Liu, Avanti Gulhane, Domenico Mastrodicasa, Wei Wu, Edward J Wang, Dushyant W Sahani, Shwetak Patel
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit training annotations. However, it is crucial to ensure that these AI models do not mirror or amplify human biases, thereby disadvantaging historically marginalized groups such as females or Black patients. The manifestation of such biases could systematically delay essential medical care for certain patient subgroups. In this study, we investigate the algorithmic fairness of state-of-the-art vision-language foundation models in chest X-ray diagnosis across five globally-sourced datasets. Our findings reveal that compared to board-certified radiologists, these foundation models consistently underdiagnose marginalized groups, with even higher rates seen in intersectional subgroups, such as Black female patients. Such demographic biases present over a wide range of pathologies and demographic attributes. Further analysis of the model embedding uncovers its significant encoding of demographic information. Deploying AI systems with these biases in medical imaging can intensify pre-existing care disparities, posing potential challenges to equitable healthcare access and raising ethical questions about their clinical application.
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）
CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（数据科学研究所，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（先进计算科学系，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；VITO, Belgium（比利时VITO研究院）
In this work we examine the performance enhancement in classification of medical imaging data when image features are combined with associated non-image data. We compare the performance of eight state-of-the-art deep neural networks in classification tasks when using only image features, compared to when these are combined with patient metadata. We utilise transfer learning with networks pretrained on ImageNet used directly as feature extractors and fine tuned on the target domain. Our experiments show that performance can be significantly enhanced with the inclusion of metadata and use interp…
Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Centre for Intelligent Machines, McGill University, Montreal, Canada（智能机器中心，麦吉尔大学，加拿大）；Mila -- Quebec AI Institute, Montreal, Canada（魁北克人工智能研究所）；Stanford University, Stanford, USA（斯坦福大学）；University of Copenhagen, Copenhagen, Denmark（哥本哈根大学）
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）
FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis
University of Pittsburgh, Swanson School of Engineering（匹兹堡大学斯旺森工程学院）；The University of North Carolina at Chapel Hill, Department of Computer Science（北卡罗来纳大学教堂山分校计算机科学系）；Arizona State University, School of Computing and Augmented Intelligence（亚利桑那州立大学计算与增强智能学院）；University of Pittsburgh, Department of Psychiatry（匹兹堡大学精神病学系）
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Rice University（莱斯大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）；McGovern Medical School, UTHealth Houston（德克萨斯大学健康科学中心休斯顿分校麦戈文医学院）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院西德尼·基梅尔综合癌症中心）；Google Research（谷歌研究院）；Department of Neurosurgery, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院神经外科）
Rutgers University, School of Arts and Sciences Newark（罗格斯大学纽瓦克文理学院）
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts' trust in the AI agents. Explainable AI may build such trust by helping medical experts to understand the AI decision processes behind diagnostic judgements. Here we introduce and evaluate explanations based on Bayesian Teaching, a formal account of explanation rooted in …
Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
Centre for Computational Imaging and Simulation Technologies in Biomedicine（生物医学计算成像与模拟技术中心）；Leeds Institute for Cardiovascular and Metabolic Medicine（利兹心血管与代谢医学研究所）；School of Medicine（医学院）；School of Computing, University of Leeds（利兹大学计算机学院）
本文提出一种多模态 Transformer 网络，整合胸部 X 射线图像与患者人口统计信息生成放射报告，实验证明加入非影像数据有效提升了生成质量。
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medicine（威尔康奈尔医学院）
Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different …
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）
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
University Hospital Bonn（波恩大学医院）；LMU University Hospital（慕尼黑莱茵河医学院医院）；University Hospital Düsseldorf（杜伊斯堡大学医院）；Heidelberg University Hospital（海德堡大学医院）；Università Cattolica del Sacro Cuore（罗马天主教圣心大学）；University Hospital Zurich（苏黎世大学医院）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；DTU Compute（技术大学计算学院）；Helmholtz AI（海德堡中心人工智能）
BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities
Mohamed Bin Zayed University of Artificial Intelligence（Mohamed Bin Zayed大学人工智能学院）；Linköping University（林肯大学）；Shaikh Tahnoon bin Mohammed Medical City（Shaikh Tahnoon bin Mohammed医疗城）；Tawam Hospital（Tawam医院）；Sheikh Shakhbout Medical City（Sheikh Shakhbout医疗城）；Govt Medical College Kozhikode（科钦政府医学院）
VinBigData JSC（VinBigData股份公司）；VinUniversity（VinUniversity（温纳大学））；VinUni-Illinois Smart Health Center（VinUni-伊利诺伊智慧健康中心）
Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective estimates about "ground truth labels" during the annotation process, depending on their expertise and experience. As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms. To tackle this challenge, we propose a simple yet effective a…
KA$^2$ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Liangzhu Laboratory, Zhejiang University（浙江大学良渚实验室）；WeDoctor Holdings Limited（微医控股有限公司）
Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentation
School of Electronics and Information Engineering, Beijing Jiaotong University, China（电子信息工程学院，北京交通大学）；Peking University School and Hospital of Stomatology, China（北京大学口腔医院）
Imperial College London（伦敦帝国学院）；Aichi Cancer Center（爱知县癌症中心）；Nagoya University（名古屋大学）
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort. However, when this concept is ported to the medical imaging domain, reading expertise will have a direct impact on the annotation accuracy. In this study, we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography (CT) image database. In controlled experiments, we study this impact for different types of w…
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology（可信自主系统研究所和南方科技大学计算机科学与工程系）；Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（高性能计算中心和深圳智能生物信息学重点实验室，深圳先进技术研究院，中国科学院）；Tomey Corporation（Tomey公司）；Changchun University（长春大学）；School of computer science, University of Nottingham Ningbo China（Nottingham Ningbo中国大学计算机学院）；School of Ophthalmology and Optometry, Wenzhou Medical University（眼科学与视光学学院，温州医科大学）
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国）；University Hospital of Strasbourg, France（斯特拉斯堡大学医院、法国）
A patient is often willing to quickly get, from his physician, reliable analysis and concise explanation according to provided linked medical images. The fact of making choices individually by the patient's physician may lead to malpractices and consequently generates unforeseeable damages. The Institute of Medicine of the National Sciences Academy(IMNAS) in USA published a study estimating that up to 98,000 hospital deathseach year can be attributed to medical malpractice [1]. Moreover, physician, in charge of medical image analysis, might be unavailable at the right time, which may complicat…
MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息学院）；School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算机与信息学院）
BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases
Johns Hopkins University（约翰霍普金斯大学）；German Cancer Research Center（德国癌症研究中心）；University Hospital Basel（巴塞尔大学医院）；University of Zurich（苏黎世大学）；ETH AI Center（苏黎世联邦理工学院AI中心）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；Stanford University（斯坦福大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Nanyang Technological University（南洋理工大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of California, San Francisco（加州大学旧金山分校）；Johns Hopkins Medicine（约翰霍普金斯医学）
Are demographically invariant models and representations in medical imaging fair?
Technical University of Denmark（丹麦技术大学）；Pioneer Centre for AI（人工智能先锋中心）；Research Institute for Signals, Systems and Computational Intelligence (CONICET)（信号、系统与计算智能研究所（CONICET））；Universidad Nacional del Litoral（国立 littoral 大学）；University of Copenhagen（哥本哈根大学）；Neurobiology Research Unit, Rigshospitalet（哥本哈根大学医院神经生物学研究单元）
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）
Deep Learning Discovery of Demographic Biomarkers in Echocardiography
Cedars-Sinai Medical Center（西达赛奈医疗中心）；Stanford University（斯坦福大学）；University of California San Francisco（加州大学旧金山分校）；San Francisco Veteran Affairs Medical Center（旧金山退伍军人事务医疗中心）
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing t…
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
Expert-layman text style transfer technologies have the potential to improve communication between members of scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online. At present, two bottlenecks interfere with the goal of building high-quality medical expert-layman style transfer systems: a dearth of pretrained medical-domain language models spanning both expert and layman t…
