Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists-Reference-Cited by-同舟云学术

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists

Published:2021-02 Issue: Volume: Page:
ISSN:0190-9622
Container-title:Journal of the American Academy of Dermatology
language:en
Short-container-title:Journal of the American Academy of Dermatology

Author:

Brinker Titus J.,Schmitt Max,Krieghoff-Henning Eva I.,Barnhill Raymond,Beltraminelli Helmut,Braun Stephan A.,Carr Richard,Fernandez-Figueras Maria-Teresa,Ferrara Gerardo,Fraitag Sylvie,Gianotti Raffaele,Llamas-Velasco Mar,Müller Cornelia S.L.,Perasole Antonio,Requena Luis,Sangueza Omar P.,Santonja Carlos,Starz Hans,Vale Esmeralda,Weyers Wolfgang,Hekler Achim,Kather Jakob N.,Fröhling Stefan,Krahl Dieter,Holland-Letz Tim,Utikal Jochen S.,Saggini Andrea,Kutzner Heinz

Funder

German Federal Ministry of Health

Publisher

Elsevier BV

Subject

Dermatology

Reference5 articles.

1. Machine learning and the cancer-diagnosis problem–no gold standard;Adamson;N Engl J Med,2019

2. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study;Tschandl;Lancet Oncol,2019

3. Pathologist-level classification of histopathological melanoma images with deep neural networks;Hekler;Eur J Cancer,2019

4. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images;Hekler;Eur J Cancer,2019

5. Digital pathology and artificial intelligence;Niazi;Lancet Oncol,2019

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin‐stained histopathology slides;Journal of the European Academy of Dermatology and Venereology;2024-08-31

2. Artificial intelligence in dermatopathology: Updates, strengths, and challenges;Clinics in Dermatology;2024-06

3. Analysis and Prediction of Skin Cancer Disease using Ensemble CNN Technique;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09

4. Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics;JAMA Dermatology;2024-03-01

5. Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms;JDDG: Journal der Deutschen Dermatologischen Gesellschaft;2024-03