Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. However, these models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones. To the best of our knowledge, limited work has been done on identifying, let alone reducing, model bias in skin disease classification and segmentation. In this paper, we examine DL fairness and demonstrate the existence of bias in classification and segmentation models for subpopulations with darker skin tones compared to individuals with lighter skin tones, for specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we propose a novel preprocessing, data alteration method, called EdgeMixup, to improve model fairness with a linear combination of an input skin lesion image and a corresponding a predicted edge detection mask combined with color saturation alteration. For the task of skin disease classification, EdgeMixup outperforms much more complex competing methods such as adversarial approaches, achieving a 10.99% reduction in accuracy gap between light and dark skin tone samples, and resulting in 8.4% improved performance for an underrepresented subpopulation.
TrustSkin: A Fairness Pipeline for Trustworthy Facial Affect Analysis Across Skin Tone
Departamento de Física, Universidad de Tarapacá（物理系，塔拉帕卡大学）；Departamento de Ciencia de la Computación, Facultad de Ingeniería, Pontificia Universidad Católica de Chile（计算机科学系，工程学院，智利天主教大学）
Colorimeter-Supervised Skin Tone Estimation from Dermatoscopic Images for Fairness Auditing
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek（奥西杰克大学电子工程、计算机科学与信息科技学院）
Exploring the Impact of Skin Color on Skin Lesion Segmentation
School of Digital and Physical Sciences at the University of Hull（赫尔大学数字与物理科学学院）；Faculty of Electrical Engineering at the University of Sarajevo（萨拉热窝大学电气工程学院）
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Artefact Research Center, Paris, France（阿切夫特研究中心，法国巴黎）；Institut de Mathématiques de Toulouse (UMR 5219), CNRS, Université de Toulouse, F-31062 Toulouse, France（图卢兹数学研究所（UMR 5219），CNRS，图卢兹大学，法国图卢兹）；L’Oréal Research（欧莱雅研究）；INRIA, ANITI, France（INRIA，ANITI，法国）
The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models
School of Biomedical Engineering and Imaging Sciences, King's College London, UK（生物医学工程与成像科学学院，伦敦国王学院，英国）；Dermatology Department, Kings College Hospital NHS Foundation Trust, London, UK（皮肤科部门，国王学院医院国家健康服务信托基金，伦敦，英国）
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）
Are generative models fair? A study of racial bias in dermatological image generation
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（瓦伦西亚理工大学 以人为本技术大学研究所）；Technical University of Denmark（丹麦技术大学）
MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification
University of Dschang（德昌大学）；Université Paris-Panthéon-Assas（巴黎先贤祠-阿萨斯大学）；Université de Picardie Jules Verne（儒勒·凡尔纳皮卡第大学）；Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）
University of Bristol（布里斯托大学）；Jean Golding Institute（让·戈尔丁研究所）；Massachusetts Institute of Technology（麻省理工学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Biomedical Data Analysis and Visualisation (BDAV) Lab, School of Computer Science（生物医学数据分析与可视化实验室，计算机科学学院）；Sydney School of Public Health（悉尼公共卫生学院）；School of Computing（计算学院）
Towards unraveling calibration biases in medical image analysis
Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）；Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET)（信号、系统与计算智能研究所sinc(i)（FICH-UNL/阿根廷国家科学与技术研究理事会））
