Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction abo...

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Autores principales:	YAN Rui, CHEN Limeng, LI Jintao, REN Fei
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Medical Journal of Peking Union Medical College Hospital 2021
Materias:	pathological images deep learning cancer classification cancer grading computer-aided diagnosis Medicine R
Acceso en línea:	https://doaj.org/article/8d286a1c33364bde8c4e0ff3e8bf4912
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spelling	oai:doaj.org-article:8d286a1c33364bde8c4e0ff3e8bf49122021-12-01T01:44:05ZResearch Progress of Cancer Classification Based on Deep Learning and Histopathological Images1674-908110.12290/xhyxzz.2021-0452https://doaj.org/article/8d286a1c33364bde8c4e0ff3e8bf49122021-10-01T00:00:00Zhttps://xhyxzz.pumch.cn/en/article/doi/10.12290/xhyxzz.2021-0452https://doaj.org/toc/1674-9081Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.YAN RuiCHEN LimengLI JintaoREN FeiEditorial Office of Medical Journal of Peking Union Medical College Hospitalarticlepathological imagesdeep learningcancer classificationcancer gradingcomputer-aided diagnosisMedicineRZHXiehe Yixue Zazhi, Vol 12, Iss 5, Pp 742-748 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	pathological images deep learning cancer classification cancer grading computer-aided diagnosis Medicine R
spellingShingle	pathological images deep learning cancer classification cancer grading computer-aided diagnosis Medicine R YAN Rui CHEN Limeng LI Jintao REN Fei Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
description	Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.
format	article
author	YAN Rui CHEN Limeng LI Jintao REN Fei
author_facet	YAN Rui CHEN Limeng LI Jintao REN Fei
author_sort	YAN Rui
title	Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
title_short	Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
title_full	Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
title_fullStr	Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
title_full_unstemmed	Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images
title_sort	research progress of cancer classification based on deep learning and histopathological images
publisher	Editorial Office of Medical Journal of Peking Union Medical College Hospital
publishDate	2021
url	https://doaj.org/article/8d286a1c33364bde8c4e0ff3e8bf4912
work_keys_str_mv	AT yanrui researchprogressofcancerclassificationbasedondeeplearningandhistopathologicalimages AT chenlimeng researchprogressofcancerclassificationbasedondeeplearningandhistopathologicalimages AT lijintao researchprogressofcancerclassificationbasedondeeplearningandhistopathologicalimages AT renfei researchprogressofcancerclassificationbasedondeeplearningandhistopathologicalimages
_version_	1718405974784475136

Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

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