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| Deep Learning in Digital Pathology Analysis |
| Yan Wen1, Tang Ye1 ,Chang Eric I-Chao2, Lai Maode3 ,Xu Yan1,2* |
1School of Biological Science and Medical Engineering, BUAA, State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing Advanced Innovation Center for Biomedical Engineering, Beijing 100191, China;
2Microsoft Research, Beijing 100080, China;
3Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, China |
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Abstract Pathology is regarded as the gold standard for diagnosis of cancer. Pathological analysis and prognosis are usually performed by pathologists, however, it could be time-consuming and labor-intensive. As the development of the whole slide pathology, it is thanks to artificial intelligence (AI)gradually promotethe transition from qualitative analysis to quantitative analysis. In recent years, the AI technology, especially deep neural network, has greatly promoted the progress of pathological diagnosis, which is more intelligentialized, accurate and repeatable. This paper describes the basic concept of deep learning and its application in digital pathology analysis. We give a brief overview of the application of deep learning in detection and segmentation of cell and tissue, classification and grading of cancer, and other applications. Finally, we propose the existing problems and the prospect of future development in the analysis of digital pathology.
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Received: 23 May 2017
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