Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data. In this paper, we propose bulk production augmentation (BPA) to generate high-quality, diverse pseudo-skin tumor images with the desired structural malignant features for additional training images from a limited number of labeled images. The proposed BPA acts as an effective data augmentation in constructing the feature detector for the atypical pigment network (APN), which is a key structure in melanoma diagnosis. Experiments show that training with images generated by our BPA largely boosts the APN detection performance by 20.0 percentage points in the area under the receiver operating characteristic curve, which is 11.5 to 13.7 points higher than that of conventional CycleGAN-based augmentations in AUC.
On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis
Technological University Dublin（都柏林理工大学）；Research Ireland Centre for Research Training in Machine Learning, ML-Labs（爱尔兰研究中心机器学习研究培训中心，机器学习实验室）；Dublin City University（都柏林城市大学）；University College Dublin（都柏林大学学院）
Early Melanoma Diagnosis with Sequential Dermoscopic Images
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University（郑州大学公共卫生学院流行病学与生物统计学系）；Victorian Melanoma Service, Alfred Health（阿尔弗雷德健康服务中心维多利亚黑色素瘤中心）；eResearch Centre, Monash University（莫纳什大学电子研究中心）
Integrating Clinical Knowledge Graphs and Gradient-Based Neural Systems for Enhanced Melanoma Diagnosis via the 7-Point Checklist
The University of British Columbia（不列颠哥伦比亚大学）；Vancouver Coastal Health Research Institute（温哥华海岸健康研究机构）；Department of Dermatology and Skin Science（皮肤科与皮肤病学系）；Photomedicine Institute（光医学研究所）；Centre for Clinical Epidemiology and Evaluation（临床流行病学与评估中心）；School of Biomedical Engineering（生物医学工程学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Population Health Sciences（人群健康科学系）；BC Cancer（不列颠哥伦比亚省癌症中心）；BC Children’s Hospital Research Institute（不列颠哥伦比亚儿童医院研究机构）；Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东生物医学测量与超声成像重点实验室）；Shenzhen University Medical School（深圳大学医学院）；Shenzhen University（深圳大学）
AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized Deep CNN architecture
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images
SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
Alliance School of Advanced Computing, Alliance University, India（印度联盟大学高级计算学院）；Department of Computer Science and Communication, Østfold University College, 1783 Halden, Norway（挪威东福尔大学学院计算机科学与传播系）；Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark（丹麦奥胡斯大学牙科与口腔健康系）
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Medical University of Vienna（维也纳医科大学）；Uniklinikum Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Bavarian Cancer Research Center (BZFK)（巴伐利亚癌症研究中心）；University Medical Center Mannheim（曼海姆大学医学中心）；Ruprecht-Karl University of Heidelberg（海德堡鲁普雷希特-卡尔大学（海德堡大学））；DKFZ Hector Cancer Institute at the University Medical Center Mannheim（曼海姆大学医学中心DKFZ赫克托癌症研究所）；University Hospital Frankfurt（法兰克福大学医院）；Goethe-University Frankfurt（法兰克福歌德大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；University Hospital Würzburg（维尔茨堡大学医院）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital Aachen（亚琛大学医院）；RWTH University of Aachen（亚琛工业大学）
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression
Proscia, Inc.（Proscia公司）；University of Florida College of Medicine（佛罗里达大学医学院）；Sidney Kimmel Medical College at Thomas Jefferson University（托马斯·杰斐逊大学西德尼·金梅尔医学院）
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Technical University Dresden（德累斯顿工业大学）；Medical University of Vienna（维也纳医科大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；Hospital Clínic of Barcelona（巴塞罗那临床医院）；University of Barcelona（巴塞罗那大学）；National & Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；Sestre milosrdnice University Hospital Center（米洛塞尔德尼采大学医院中心）；Derma Style（Derma Style皮肤科诊所）；Dubai London Clinic（迪拜伦敦诊所）；West Dermatology；University of Rijeka（里耶卡大学）；Clinical Hospital Center Rijeka（里耶卡临床医院中心）；LaserMed；Gazi University（加齐大学）；Institut Curie（居里研究所）；University of Paris（巴黎大学）；Universidad Autónoma de Madrid（马德里自治大学）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University Duisburg-Essen（杜伊斯堡-埃森大学）；University Hospital Essen（埃森大学医院）；Technische Universität Dresden（德累斯顿工业大学）；University Hospital Carl Gustav Carus（卡尔·古斯塔夫·卡鲁斯大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Uniklinikum Erlangen（埃尔兰根大学医院）；University Hospital Regensburg（雷根斯堡大学医院）；University Hospital Würzburg（维尔茨堡大学医院）
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
Louisiana State University in Shreveport（路易斯安那州立大学什里夫波特分校）；Kitware（Kitware公司）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Missouri University of Science and Technology（密苏里科技大学）；Emory University（埃默里大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）
Impact of Data Augmentation on Confidence Calibration in Melanoma Classification
Technological University Dublin（都柏林理工大学）；Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究培训中心）；University College Dublin（都柏林大学学院）；Dublin City University（都柏林城市大学）
A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth
Proscia Inc.（Proscia公司）；University of Florida College of Medicine（佛罗里达大学医学院）；Sidney Kimmel Medical College at Thomas Jefferson University（托马斯杰斐逊大学西德尼·金梅尔医学院）；Cedars-Sinai（西达赛奈医疗中心）
Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg（海德堡鲁普雷希特-卡尔大学曼海姆医学中心）；DKFZ Hector Cancer Institute at the University Medical Center Mannheim（曼海姆医学中心DKFZ赫克托癌症研究所）；Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院暨大学医院）；University Hospital (UKSH), Kiel（基尔大学医院（UKSH））
REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；Vietnam National University（越南国立大学）；University of Science, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市科学大学）；International University, Vietnam National University Ho Chi Minh City（越南国立大学胡志明市国际大学）
