Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

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作者： Hyun-Jong,Jang ^[1] ; Ahwon,Lee ^[2] ; J,Kang ^[2] ; In Hye,Song ^[2] ; Sung Hak,Lee ^[3]

作者单位： Department of Physiology, Department of Biomedicine and Health Sciences, Catholic Neuroscience Institute, The Catholic University of Korea, Seoul 06591, South Korea. ^[1] Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea. ^[2] Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea. hakjjang@catholic.ac.kr. ^[3]

关键词 Colorectal cancer Computational pathology Computer-aided diagnosis Deep learning Digital pathology Mutation

主题词结肠肿瘤(Colonic Neoplasms);结直肠肿瘤(Colorectal Neoplasms);基因, p53(Genes, p53);人类(Humans);突变(Mutation)

DOI 10.3748/wjg.v26.i40.6207

PMID 33177794

发布时间 2024-03-30

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World journal of gastroenterology

2020年26卷40期

6207-6223页

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Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

World journal of gastroenterology

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World journal of gastroenterology

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Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

World journal of gastroenterology

相似文献

World journal of gastroenterology

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检索历史清除