Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns-Reference-Cited by-同舟云学术

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns

Published:2020-11-13 Issue:5 Volume:34 Page:895-903
ISSN:0893-3952
Container-title:Modern Pathology
language:en
Short-container-title:Mod Pathol

Author:

Kimeswenger Susanne,Tschandl Philipp,Noack Petar,Hofmarcher Markus,Rumetshofer Elisabeth,Kindermann Harald,Silye Rene,Hochreiter Sepp,Kaltenbrunner Martin,Guenova Emmanuella^ORCID,Klambauer Guenter,Hoetzenecker Wolfram

Publisher

Springer Science and Business Media LLC

Subject

Pathology and Forensic Medicine

Link

http://www.nature.com/articles/s41379-020-00712-7.pdf

Reference31 articles.

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3. Arevalo J, Cruz-roa A, Arias V, Romero E, González FA. An unsupervised feature learning framework for basal cell carcinoma image analysis. Artif Intell Med. 2015;64:131–45.

4. Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep. 2017;7:1–14.

5. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck V, Silva K, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25:1301–9.

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