医学
淋巴结
H&E染色
接收机工作特性
放射科
乳腺癌
深度学习
算法
淋巴
癌症
试验装置
病理
人工智能
内科学
机器学习
染色
计算机科学
作者
Babak Ehteshami Bejnordi,Mitko Veta,Paul Johannes van Diest,Bram van Ginneken,Nico Karssemeijer,Geert Litjens,Jeroen van der Laak,Meyke Hermsen,Quirine F. Manson,Maschenka Balkenhol,Oscar Geessink,Nikolas Stathonikos,Marcory CRF van Dijk,Peter Bult,Francisco Beça,Andrew H. Beck,D. Wang,Aditya Khosla,Rishab Gargeya,Humayun Irshad
出处
期刊:JAMA
[American Medical Association]
日期:2017-12-12
卷期号:318 (22): 2199-2199
被引量:3060
标识
DOI:10.1001/jama.2017.14585
摘要
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
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