Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a promine...

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Auteurs principaux:	Tao Li, Peizhen Xie, Jie Liu, Mingliang Chen, Shuang Zhao, Wenjie Kang, Ke Zuo, Fangfang Li
Format:	article
Langue:	EN
Publié:	Hindawi Limited 2021
Sujets:	Medicine (General) R5-920 Medical technology R855-855.5
Accès en ligne:	https://doaj.org/article/054060776d4d41d3bc4f69e6fe2b5f0f
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spelling	oai:doaj.org-article:054060776d4d41d3bc4f69e6fe2b5f0f2021-11-08T02:35:19ZAutomated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study2040-230910.1155/2021/5972962https://doaj.org/article/054060776d4d41d3bc4f69e6fe2b5f0f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5972962https://doaj.org/toc/2040-2309In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.Tao LiPeizhen XieJie LiuMingliang ChenShuang ZhaoWenjie KangKe ZuoFangfang LiHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Tao Li Peizhen Xie Jie Liu Mingliang Chen Shuang Zhao Wenjie Kang Ke Zuo Fangfang Li Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
description	In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.
format	article
author	Tao Li Peizhen Xie Jie Liu Mingliang Chen Shuang Zhao Wenjie Kang Ke Zuo Fangfang Li
author_facet	Tao Li Peizhen Xie Jie Liu Mingliang Chen Shuang Zhao Wenjie Kang Ke Zuo Fangfang Li
author_sort	Tao Li
title	Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
title_short	Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
title_full	Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
title_fullStr	Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
title_full_unstemmed	Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study
title_sort	automated diagnosis and localization of melanoma from skin histopathology slides using deep learning: a multicenter study
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/054060776d4d41d3bc4f69e6fe2b5f0f
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_version_	1718443157337669632

Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study

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