Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries

被引：1
|
作者：
McAlpine, Ewen [1 ,2 ]
Michelow, Pamela [1 ,2 ]
机构：
[1] Univ Witwatersrand, Sch Pathol, Div Anat Pathol, Johannesburg, South Africa
[2] Natl Hlth Lab Serv, Dept Anat Pathol, Johannesburg, South Africa
来源：
ADVANCES IN ANATOMIC PATHOLOGY | 2020年 / 27卷 / 04期
关键词：
digital pathology; artificial intelligence; machine learning;
D O I：
10.1097/PAP.0000000000000265
中图分类号：
R36 [病理学];
学科分类号：
100104 ;
摘要：
The application of artificial intelligence technologies to anatomic pathology has the potential to transform the practice of pathology, but, despite this, many pathologists are unfamiliar with how these models are created, trained, and evaluated. In addition, many pathologists may feel that they do not possess the necessary skills to allow them to embark on research into this field. This article aims to act as an introductory tutorial to illustrate how to create, train, and evaluate simple artificial learning models (neural networks) on histopathology data sets in the programming languagePythonusing the popular freely available, open-source librariesKeras,TensorFlow,PyTorch, andDetecto. Furthermore, it aims to introduce pathologists to commonly used terms and concepts used in artificial intelligence.
引用
收藏
页码：260 / 268
页数：9
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