{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:46:50Z","timestamp":1790725610687,"version":"4.1.0"},"reference-count":42,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Dermatol"],"published-print":{"date-parts":[[2024,3,1]]},"abstract":"<jats:sec id=\"ab-doi230069-4\">\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>The development of artificial intelligence (AI)\u2013based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-5\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-6\">\n                    <jats:title>Design, Setting, and Participants<\/jats:title>\n                    <jats:p>This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-7\">\n                    <jats:title>Exposures<\/jats:title>\n                    <jats:p>All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-8\">\n                    <jats:title>Main Outcomes and Measures<\/jats:title>\n                    <jats:p>The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-9\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;amp;lt;\u2009.001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;amp;lt;\u2009.001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-doi230069-10\">\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1001\/jamadermatol.2023.5550","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T11:01:57Z","timestamp":1707303717000},"page":"303","source":"Crossref","is-referenced-by-count":67,"title":["Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics"],"prefix":"10.1001","volume":"160","author":[{"given":"Sarah","family":"Haggenm\u00fcller","sequence":"first","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Schmitt","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Krieghoff-Henning","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achim","family":"Hekler","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman C.","family":"Maron","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Wies","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jochen S.","family":"Utikal","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karls University of Heidelberg, Mannheim, Germany"},{"name":"Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany"},{"name":"DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Friedegund","family":"Meier","sequence":"additional","affiliation":[{"name":"Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universit\u00e4t Dresden, Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Hobelsberger","sequence":"additional","affiliation":[{"name":"Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universit\u00e4t Dresden, Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank F.","family":"Gellrich","sequence":"additional","affiliation":[{"name":"Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universit\u00e4t Dresden, Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mildred","family":"Sergon","sequence":"additional","affiliation":[{"name":"Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universit\u00e4t Dresden, Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Hauschild","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital (UKSH), Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars E.","family":"French","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany"},{"name":"Dr Phillip Frost Department of Dermatology and Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucie","family":"Heinzerling","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany"},{"name":"Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen\u2013European Metropolitan Region N\u00fcrnberg, CCC Alliance WERA, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin G.","family":"Schlager","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamran","family":"Ghoreschi","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, Corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Schlaak","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, Corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franz J.","family":"Hilke","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, Corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela","family":"Poch","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, Corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f6ren","family":"Korsing","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin, Corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carola","family":"Berking","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen\u2013European Metropolitan Region N\u00fcrnberg, CCC Alliance WERA, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus V.","family":"Heppt","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen\u2013European Metropolitan Region N\u00fcrnberg, CCC Alliance WERA, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Erdmann","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen\u2013European Metropolitan Region N\u00fcrnberg, CCC Alliance WERA, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Haferkamp","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Regensburg, Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Drexler","sequence":"additional","affiliation":[{"name":"Department of Dermatology, University Hospital Regensburg, Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Schadendorf","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wiebke","family":"Sondermann","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Goebeler","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, University Hospital W\u00fcrzburg and National Center for Tumor Diseases (NCT) WERA, W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastian","family":"Schilling","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology, University Hospital W\u00fcrzburg and National Center for Tumor Diseases (NCT) WERA, W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob N.","family":"Kather","sequence":"additional","affiliation":[{"name":"Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Fr\u00f6hling","sequence":"additional","affiliation":[{"name":"Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Titus J.","family":"Brinker","sequence":"additional","affiliation":[{"name":"Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","reference":[{"issue":"7788","key":"doi230069r1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening.","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"issue":"1","key":"doi230069r2","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","article-title":"Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.","volume":"28","author":"Bulten","year":"2022","journal-title":"Nat Med"},{"issue":"8","key":"doi230069r3","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1038\/s41591-020-0931-3","article-title":"Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.","volume":"26","author":"Mei","year":"2020","journal-title":"Nat Med"},{"issue":"7639","key":"doi230069r4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks.","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"doi230069r5","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.ejca.2021.06.049","article-title":"Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.","volume":"156","author":"Haggenm\u00fcller","year":"2021","journal-title":"Eur J Cancer"},{"issue":"9","key":"doi230069r6","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1016\/j.jid.2020.01.019","article-title":"Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.","volume":"140","author":"Han","year":"2020","journal-title":"J Invest Dermatol"},{"issue":"1","key":"doi230069r7","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.annonc.2019.10.013","article-title":"Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.","volume":"31","author":"Haenssle","year":"2020","journal-title":"Ann Oncol"},{"issue":"10151","key":"doi230069r8","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/S0140-6736(18)31559-9","article-title":"Melanoma.","volume":"392","author":"Schadendorf","year":"2018","journal-title":"Lancet"},{"key":"doi230069r9","doi-asserted-by":"publisher","first-page":"j2813","DOI":"10.1136\/bmj.j2813","article-title":"Pathologists\u2019 diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study.","volume":"357","author":"Elmore","year":"2017","journal-title":"BMJ"},{"issue":"4","key":"doi230069r10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1111\/cup.2008.35.issue-4","article-title":"Discordance in the histopathologic diagnosis of difficult melanocytic neoplasms in the clinical setting.","volume":"35","author":"Lodha","year":"2008","journal-title":"J Cutan Pathol"},{"issue":"8","key":"doi230069r11","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1093\/annonc\/mdy166","article-title":"Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.","volume":"29","author":"Haenssle","year":"2018","journal-title":"Ann Oncol"},{"issue":"3","key":"doi230069r12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0193321","article-title":"Acral melanoma detection using a convolutional neural network for dermoscopy images.","volume":"13","author":"Yu","year":"2018","journal-title":"PLoS One"},{"issue":"7","key":"doi230069r13","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","article-title":"Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.","volume":"20","author":"Tschandl","year":"2019","journal-title":"Lancet Oncol"},{"issue":"3","key":"doi230069r14","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.jaad.2019.07.016","article-title":"Computer algorithms show potential for improving dermatologists\u2019 accuracy to diagnose cutaneous melanoma: results of the International Skin Imaging Collaboration 2017.","volume":"82","author":"Marchetti","year":"2020","journal-title":"J Am Acad Dermatol"},{"key":"doi230069r15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ejca.2019.06.012","article-title":"Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.","volume":"118","author":"Hekler","year":"2019","journal-title":"Eur J Cancer"},{"issue":"3","key":"doi230069r16","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1016\/j.jaad.2021.02.009","article-title":"Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists.","volume":"86","author":"Brinker","year":"2022","journal-title":"J Am Acad Dermatol"},{"issue":"10","key":"doi230069r17","doi-asserted-by":"publisher","first-page":"e654","DOI":"10.1016\/S2589-7500(21)00133-3","article-title":"Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.","volume":"3","author":"Muti","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"8","key":"doi230069r18","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"issue":"4","key":"doi230069r19","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1053\/j.gastro.2020.06.021","article-title":"Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning.","volume":"159","author":"Echle","year":"2020","journal-title":"Gastroenterology"},{"issue":"7862","key":"doi230069r20","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-021-03583-3","article-title":"Swarm learning for decentralized and confidential clinical machine learning.","volume":"594","author":"Warnat-Herresthal","year":"2021","journal-title":"Nature"},{"issue":"1","key":"doi230069r21","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/MNET.65","article-title":"A blockchain-based decentralized federated learning framework with committee consensus.","volume":"35","author":"Li","year":"2021","journal-title":"IEEE Netw"},{"key":"doi230069r22","doi-asserted-by":"publisher","first-page":"011","DOI":"10.59275\/j.melba.2022-1a3f","article-title":"Semi-supervised federated peer learning for skin lesion classification.","volume":"1","author":"Bdair","year":"2022","journal-title":"MELBA J"},{"issue":"1","key":"doi230069r24","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1038\/s41598-022-05539-7","article-title":"Federated learning and differential privacy for medical image analysis.","volume":"12","author":"Adnan","year":"2022","journal-title":"Sci Rep"},{"issue":"10","key":"doi230069r25","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19.","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat Med"},{"issue":"6","key":"doi230069r26","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1038\/s41591-022-01768-5","article-title":"Swarm learning for decentralized artificial intelligence in cancer histopathology.","volume":"28","author":"Saldanha","year":"2022","journal-title":"Nat Med"},{"key":"doi230069r27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102298","article-title":"Federated learning for computational pathology on gigapixel whole slide images.","volume":"76","author":"Lu","year":"2022","journal-title":"Med Image Anal"},{"key":"doi230069r28","doi-asserted-by":"publisher","first-page":"h5527","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies.","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"issue":"1","key":"doi230069r29","doi-asserted-by":"publisher","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis.","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci Rep"},{"issue":"2","key":"doi230069r34","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/info11020108","article-title":"Fastai: a layered API for deep learning.","volume":"11","author":"Howard","year":"2020","journal-title":"Information"},{"issue":"7","key":"doi230069r37","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","article-title":"Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.","volume":"25","author":"Kather","year":"2019","journal-title":"Nat Med"},{"key":"doi230069r41","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejca.2022.02.025","article-title":"Explainable artificial intelligence in skin cancer recognition: a systematic review.","volume":"167","author":"Hauser","year":"2022","journal-title":"Eur J Cancer"},{"key":"doi230069r42","doi-asserted-by":"publisher","first-page":"233","DOI":"10.3389\/fmed.2020.00233","article-title":"Artificial intelligence in skin cancer diagnostics: the patients\u2019 perspective.","volume":"7","author":"Jutzi","year":"2020","journal-title":"Front Med (Lausanne)"},{"key":"doi230069r23","author":"Agbley","year":"2021"},{"key":"doi230069r31","author":"Akiba"},{"key":"doi230069r33","volume-title":"Advances in Neural Information Processing Systems","author":"Paszke","year":"2019","edition":"Vol 32"},{"key":"doi230069r35","doi-asserted-by":"crossref","DOI":"10.1201\/9780429246593","volume-title":"An Introduction to the Bootstrap","author":"Efron","year":"1994"},{"key":"doi230069r30","unstructured":"Bergstra? J, Bardenet? R, Bengio? Y, K\u00e9gl? B. Algorithms for hyper-parameter optimization. Accessed March 4, 2023. https:\/\/proceedings.neurips.cc\/paper\/2011\/file\/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf"},{"key":"doi230069r32","unstructured":"Smith? LN. A disciplined approach to neural network hyper-parameters: part 1\u2013learning rate, batch size, momentum, and weight decay.? arXiv. Preprint posted online March 26, 2018. doi:10.48550\/arXiv.1803.09820"},{"key":"doi230069r36","unstructured":"McMahan? HB, Moore? E, Ramage? D, Hampson? S, Arcas? BAY. Communication-efficient learning of deep networks from decentralized data.? arXiv. Preprint posted online February 17, 2016. doi:10.48550\/arXiv.1602.05629"},{"key":"doi230069r38","unstructured":"Maji? D, Santara? A, Mitra? P, Sheet? D. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images.? arXiv. Preprint posted online March 15, 2016. doi:10.48550\/arXiv.1603.04833"},{"key":"doi230069r39","unstructured":"Leitlinienprogramm Onkologie. Diagnostik, therapie und nachsorge des melanoms. Langversion 3.3; July 2020, AWMF Registernummer: 032\/024OL. Publication in German. Accessed August 29, 2023. https:\/\/www.leitlinienprogramm-onkologie.de\/fileadmin\/user_upload\/Downloads\/Leitlinien\/Melanom\/Melanom_Version_3\/LL_Melanom_Langversion_3.3.pdf"},{"key":"doi230069r40","unstructured":"Kairouz? P, McMahan? HB, Avent? B, . Advances and open problems in federated learning.? arXiv. Preprint posted online March 9, 2021. doi:10.48550\/arXiv.1912.04977"}],"container-title":["JAMA Dermatology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamadermatology\/articlepdf\/2814691\/jamadermatology_haggenmller_2024_oi_230069_1710184555.25589.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T11:01:54Z","timestamp":1710932514000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamadermatology\/fullarticle\/2814691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,1]]}},"URL":"https:\/\/doi.org\/10.1001\/jamadermatol.2023.5550","relation":{},"ISSN":["2168-6068"],"issn-type":[{"value":"2168-6068","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3,1]]}}}