In the realm of dermatology, the complexity of diagnosing skin conditions manually necessitates the expertise of dermatologists. Accurate identification of various skin ailments, ranging from cancer to inflammatory diseases, is paramount. However, existing artificial intelligence (AI) models in dermatology face challenges, particularly in accurately diagnosing diseases across diverse skin tones, with a notable performance gap in darker skin. Additionally, the scarcity of publicly available, unbiased datasets hampers the development of inclusive AI diagnostic tools. To tackle the challenges in accurately predicting skin conditions across diverse skin tones, we employ a transfer-learning approach that capitalizes on the rich, transferable knowledge from various image domains. Our method integrates multiple pre-trained models from a wide range of sources, including general and specific medical images, to improve the robustness and inclusiveness of the skin condition predictions. We rigorously evaluated the effectiveness of these models using the Diverse Dermatology Images (DDI) dataset, which uniquely encompasses both underrepresented and common skin tones, making it an ideal benchmark for assessing our approach. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, we conducted domain adaptation using additional skin image datasets such as HAM10000. This adaptation significantly improved model performance across all models.
A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Lab（湖畔实验室）；Beijing Normal University（北京师范大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；The Second Affiliated Hospital Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；Xiangya Hospital Central South University（中南大学湘雅医院）
An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models
Department of Computer Science and Engineering, Faridpur Engineering College, Faridpur, Bangladesh（孟加拉国法里德普尔工程学院计算机科学与工程系）；Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh（孟加拉国达卡大学计算机科学与工程系）；Department of Oncology, Labaid Cancer and Super Speciality Center, Dhaka, Bangladesh（孟加拉国达卡拉贝德癌症与超级专科中心肿瘤科）；Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh（孟加拉国达卡大学信息技术研究所）
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
Stanford School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Keio University School of Medicine（庆应义塾大学医学院）；Chan-Zuckerberg Biohub（陈-扎克伯格生物中心）
SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images
Nanyang Technological University（南洋理工大学）；University of British Columbia（不列颠哥伦比亚大学）；Vancouver Coast Health Research Institute（温哥华海岸健康研究所）；BC Cancer（不列颠哥伦比亚癌症中心）
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images
Université Clermont Auvergne（克莱蒙奥弗涅大学）；CNRS（法国国家科学研究中心）；ENSMSE（圣艾蒂安国立矿业工程师学校）；INRAE（法国国家农业食品与环境研究院）；VetAgro Sup（法国兽医与农学高等学院）；Université de Lyon（里昂大学）；CHU Clermont-Ferrand（克莱蒙费朗大学医院）；Inserm（法国国家健康与医学研究院）；Tous Chercheurs Laboratory（全民研究者实验室）；CPIE Champenoux（尚佩努环境倡议中心）；University Hospital of Nancy（南锡大学医院）；University Hospital of Saint Etienne（圣艾蒂安大学医院）；IHU-Méditerranée Infection（地中海感染大学医院研究所）；Aix Marseille Univ（艾克斯-马赛大学）；IRD（法国发展研究院）；AP-HM（马赛公立医院援助机构）；SSA（法国军队卫生勤务局）；Hôpitaux Universitaires（斯特拉斯堡大学医院）；Pontchaillou University Hospital（蓬查卢大学医院）；CHR Orléans（奥尔良地区中心医院）；Hospital of Villeneuve-Saint-Georges（维勒讷夫圣乔治医院）；Centre Hospitalier Universitaire de Rennes（雷恩大学医院）
DermETAS-SNA LLM: A Dermatology Focused Evolutionary Transformer Architecture Search with StackNet Augmented LLM Assistant
Data Science Program, Worcester Polytechnic Institute, Massachusetts, USA（沃斯特理工学院数据科学项目，马萨诸塞州，美国）；Computer Science Department, Worcester Polytechnic Institute, Massachusetts, USA（沃斯特理工学院计算机科学系，马萨诸塞州，美国）；PASE Advisory Group, New Jersey, USA（PASE顾问小组，新泽西州，美国）
SkinCaRe: A Multimodal Dermatology Dataset Annotated with Medical Caption and Chain-of-Thought Reasoning
School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK–Shenzhen)（数据科学学院，香港中文大学（深圳））；Computer Science Program, CEMSE Division, King Abdullah University of Science and Technology (KAUST)（计算机科学项目，科学与工程学院，国王 Abdullah 科学技术大学）；Center of Excellence on Smart Health, KAUST（智能健康卓越中心，国王 Abdullah 科学技术大学）；Center of Excellence for Generative AI, KAUST（生成式人工智能卓越中心，国王 Abdullah 科学技术大学）；Department of Dermatology, Beijing AnZhen Hospital, Capital Medical University（皮肤科，北京安贞医院，首都医科大学）；Department of Dermatology, Tianjin Institute of Integrative Dermatology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital（皮肤科，天津整合皮肤科研究院，天津中医药大学附属医院）；Department of Dermatology, Beijing Aerospace General Hospital（皮肤科，北京航天总医院）；Department of Dermatology, The First Affiliated Hospital, Shantou University Medical College（皮肤科，汕头大学医学院第一附属医院）；DermAssure, LLC（DermAssure 公司）；School of Medicine, New York Medical College（医学院，纽约医学院）；Capital Medical University（首都医科大学）；Department of Dermatology, Second Hospital of Jilin University（皮肤科，吉林大学第二医院）；Emergency Critical Care Center, Beijing AnZhen Hospital, Capital Medical University（急诊重症中心，北京安贞医院，首都医科大学）
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning
Varendra University（瓦伦德拉大学）；Khulna University of Engineering & Technology（库尔纳工程技术大学）；Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；Rajshahi University（拉杰沙希大学）
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
Amasya University（阿马西亚大学）；Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center, Bilkent University（比尔肯特大学国家磁共振研究中心）；Sabuncu Brain Research Center, Bilkent University（比尔肯特大学萨本朱脑研究中心）
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
College of Science and Engineering, University of Galway（戈尔韦大学科学与工程学院）；School of Computer Science, University of Galway（戈尔韦大学计算机科学学院）；Trinity College Dublin, the University of Dublin（都柏林大学圣三一学院）；St James’s Hospital（圣詹姆斯医院）
本文提出 Derm-T2IM，用调优的 Stable Diffusion 生成多样化皮肤镜病变图像，并验证其可增强 CNN 与 ViT 在两个真实数据集上的分类鲁棒性和泛化性。
FairSkin: Fair Diffusion for Skin Disease Image Generation
Arizona State University（亚利桑那州立大学）；University of Pittsburgh（匹兹堡大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Michigan State University（密歇根州立大学）
An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases
School of Medicine, Xiamen University（厦门大学医学院）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Data Mining Research Center, Xiamen University（厦门大学数据挖掘研究中心）；School of Economics and Management, East China Jiaotong University（华东交通大学经济管理学院）；Department of Pediatrics, the First Affiliated Hospital of Xiamen University（厦门大学附属第一医院儿科）；School of Management, Xiamen University（厦门大学管理学院）；School of Science, Hong Kong University of Science and Technology（香港科技大学理学院）；School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；National Economic Engineering Laboratory, Dongbei University of Finance and Economics（东北财经大学国民经济工程实验室）
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
Hong Kong Baptist University（香港浸会大学）；Harbin Institute of Technology（哈尔滨工业大学）；The University of Hong Kong - Shenzhen Hospital（香港大学深圳医院）；Shenzhen Yiyuan Intelligent Technology Co., Ltd.（深圳怡园智能科技有限公司）
Exploring the Challenge and Value of Deep Learning in Automated Skin Disease Diagnosis
Polytechnic Institute, Zhejiang University（浙江大学 polytechnic 院）；School of Social and Public Administration, East China University Of Science And Technology（东华大学社会科学与公共管理学院）；College of Science, National University of Defense Technology（国防科技大学科学学院）；School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）
SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Department of Dermatology, Tianjin Institute of Integrative Dermatology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital（天津中医药大学附属医院天津市中西医结合皮肤病研究所皮肤科）；Department of Dermatology, Beijing AnZhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院皮肤科）
Adaptive Hybrid Optimizer based Framework for Lumpy Skin Disease Identification
Department of Computer Science, NUST Balochistan Campus, NUST, Pakistan（巴基斯坦努斯大学巴尔奇斯坦校区计算机科学系）；Australian e-Health Research Centre (AEHRC), CSIRO, Brisbane, Australia（澳大利亚电子健康研究中心（AEHRC）、CSIRO、布里斯班，澳大利亚）；Queensland University of Technology, Brisbane, Australia（昆士兰技术大学、布里斯班，澳大利亚）；CQI Lab, Department of Electronics and Information Convergence Engineering, Kyung Hee University, Global Campus, South Korea（韩国庆熙大学全球校区电子与信息融合工程系CQI实验室）
AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection
Department of Business Analytics/Data Science Engineering University of New Haven（商业分析/数据科学工程系 罗德岛大学）；Department of Healthcare University of New Haven（医疗健康系 罗德岛大学）
Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?
Carnegie Mellon University（卡内基梅隆大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Australian National University（澳大利亚国立大学）；University of Western Australia（西澳大学）；POSTECH；Yale University（耶鲁大学）
Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
