{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:13:22Z","timestamp":1791951202197,"version":"4.3.4"},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T00:00:00Z","timestamp":1561593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejcancer.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Cancer"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1016\/j.ejca.2019.06.012","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T23:13:31Z","timestamp":1563405211000},"page":"91-96","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":267,"special_numbering":"C","title":["Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images"],"prefix":"10.1016","volume":"118","author":[{"given":"Achim","family":"Hekler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jochen S.","family":"Utikal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander H.","family":"Enk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wiebke","family":"Solass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Schmitt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Klode","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3524-7858","authenticated-orcid":false,"given":"Dirk","family":"Schadendorf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wiebke","family":"Sondermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cindy","family":"Franklin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Bestvater","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Flaig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dieter","family":"Krahl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christof","family":"von Kalle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Fr\u00f6hling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Titus J.","family":"Brinker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10151","key":"10.1016\/j.ejca.2019.06.012_bib1","first-page":"971","volume":"392","author":"Schadendorf","year":"2018","journal-title":"Ugurel SJTL: Melanoma"},{"key":"10.1016\/j.ejca.2019.06.012_bib2","series-title":"Silvers DNJJocp: discordance in the histopathologic diagnosis of difficult melanocytic neoplasms in the clinical setting","first-page":"349","volume":"vol. 35","author":"Lodha","year":"2008"},{"key":"10.1016\/j.ejca.2019.06.012_bib3","series-title":"Faraggiana TJJocO: interobserver variability on the histopathologic diagnosis of cutaneous melanoma and other pigmented skin lesions","first-page":"1218","volume":"vol. 14","author":"Corona","year":"1996"},{"issue":"10","key":"10.1016\/j.ejca.2019.06.012_bib4","doi-asserted-by":"crossref","first-page":"e11936","DOI":"10.2196\/11936","article-title":"Skin cancer classification using convolutional neural networks: systematic review","volume":"20","author":"Brinker","year":"2018","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.ejca.2019.06.012_bib5","doi-asserted-by":"crossref","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","author":"Tschandl","year":"2019","journal-title":"Lancet Oncol"},{"key":"10.1016\/j.ejca.2019.06.012_bib6","article-title":"Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: a call for prospective data","author":"Sondermann","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejca.2019.06.013","article-title":"Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks","author":"Maron","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejca.2019.05.023","article-title":"Deep neural networks are superior to dermatologists in melanoma image classification","author":"Brinker","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejca.2019.07.019","article-title":"Superior skin cancer classification by the combination of human and artificial intelligence","author":"Hekler","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib10","series-title":"Fr\u00f6hling SJEJoC: a convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task","first-page":"148","volume":"vol. 111","author":"Brinker","year":"2019"},{"issue":"7639","key":"10.1016\/j.ejca.2019.06.012_bib11","doi-asserted-by":"crossref","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"},{"issue":"8","key":"10.1016\/j.ejca.2019.06.012_bib12","doi-asserted-by":"crossref","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":"2","key":"10.1016\/j.ejca.2019.06.012_bib13","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jaad.2017.08.016","article-title":"Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images","volume":"78","author":"Marchetti","year":"2018","journal-title":"J Am Acad Dermatol"},{"key":"10.1016\/j.ejca.2019.06.012_bib14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","article-title":"Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task","volume":"113","author":"Brinker","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib15","series-title":"Balkenhol MJJ: diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","first-page":"2199","volume":"vol. 318","author":"Bejnordi","year":"2017"},{"key":"10.1016\/j.ejca.2019.06.012_bib16","series-title":"Snyder MJNc: predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","first-page":"12474","volume":"vol. 7","author":"Yu","year":"2016"},{"key":"10.1016\/j.ejca.2019.06.012_bib17","article-title":"Pathologist-level classification of histopathological melanoma images with deep neural networks","volume":"113","author":"Hekler","year":"2019","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.ejca.2019.06.012_bib18","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ejca.2018.12.016","article-title":"Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark","volume":"111","author":"Brinker","year":"2019","journal-title":"Eur J Cancer"},{"issue":"3","key":"10.1016\/j.ejca.2019.06.012_bib19","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"10.1016\/j.ejca.2019.06.012_bib20","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition: 2016","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"3","key":"10.1016\/j.ejca.2019.06.012_bib21","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1001\/jamaoncol.2017.5449","article-title":"Not just digital pathology, intelligent digital pathology","volume":"4","author":"Acs","year":"2018","journal-title":"JAMA Oncology"},{"key":"10.1016\/j.ejca.2019.06.012_bib22","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: challenges and opportunities","volume":"33","author":"Madabushi","year":"2016","journal-title":"Med Image Anal"},{"key":"10.1016\/j.ejca.2019.06.012_bib23","series-title":"Artificial intelligence-based breast cancer nodal metastasis detection","author":"Liu","year":"2018"},{"key":"10.1016\/j.ejca.2019.06.012_bib24","article-title":"Sgdr: stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv preprint arXiv:1608.03983"}],"container-title":["European Journal of Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0959804919303806?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0959804919303806?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T20:25:41Z","timestamp":1760732741000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0959804919303806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":24,"alternative-id":["S0959804919303806"],"URL":"https:\/\/doi.org\/10.1016\/j.ejca.2019.06.012","relation":{},"ISSN":["0959-8049"],"issn-type":[{"value":"0959-8049","type":"print"}],"subject":[],"published":{"date-parts":[[2019,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images","name":"articletitle","label":"Article Title"},{"value":"European Journal of Cancer","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejca.2019.06.012","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}]}}