NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge...

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Autores principales:	Xin Liao, Qin Huang, Xin Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/57f55d0f70c64cbcb8268deb53b213b6
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spelling	oai:doaj.org-article:57f55d0f70c64cbcb8268deb53b213b62021-11-08T02:36:10ZNECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images1939-012210.1155/2021/5868501https://doaj.org/article/57f55d0f70c64cbcb8268deb53b213b62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5868501https://doaj.org/toc/1939-0122As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing deep convolution network for training and analysis. Therefore, the construction of a neural network to realize the automatic screening of cervical NEC is challenging; meanwhile, as far as we know, little attention has been paid to this field. In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion Analyzer module. A pathological WSI dataset, which is composed of 84 NEC cases and 216 NEC-free cases from the Pathological Department of West China Second University Hospital, is applied to evaluate the performance of the method. The experimental results show that the recall rate, accuracy rate, and precision rate of our method for automatic recognition are 92.9%, 92.7%, and 83.0%, respectively, demonstrating the effectiveness and the potential in clinical practice. The application of this method in computer-assisted pathological diagnosis is expected to decrease the misdiagnosis as well as the false diagnosis of rare cervical NEC, and, consequently, improve the therapeutic effect of cervical cancers.Xin LiaoQin HuangXin ZhengHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Xin Liao Qin Huang Xin Zheng NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
description	As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing deep convolution network for training and analysis. Therefore, the construction of a neural network to realize the automatic screening of cervical NEC is challenging; meanwhile, as far as we know, little attention has been paid to this field. In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion Analyzer module. A pathological WSI dataset, which is composed of 84 NEC cases and 216 NEC-free cases from the Pathological Department of West China Second University Hospital, is applied to evaluate the performance of the method. The experimental results show that the recall rate, accuracy rate, and precision rate of our method for automatic recognition are 92.9%, 92.7%, and 83.0%, respectively, demonstrating the effectiveness and the potential in clinical practice. The application of this method in computer-assisted pathological diagnosis is expected to decrease the misdiagnosis as well as the false diagnosis of rare cervical NEC, and, consequently, improve the therapeutic effect of cervical cancers.
format	article
author	Xin Liao Qin Huang Xin Zheng
author_facet	Xin Liao Qin Huang Xin Zheng
author_sort	Xin Liao
title	NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
title_short	NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
title_full	NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
title_fullStr	NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
title_full_unstemmed	NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
title_sort	necscannet: novel method for cervical neuroendocrine cancer screening from whole slide images
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/57f55d0f70c64cbcb8268deb53b213b6
work_keys_str_mv	AT xinliao necscannetnovelmethodforcervicalneuroendocrinecancerscreeningfromwholeslideimages AT qinhuang necscannetnovelmethodforcervicalneuroendocrinecancerscreeningfromwholeslideimages AT xinzheng necscannetnovelmethodforcervicalneuroendocrinecancerscreeningfromwholeslideimages
_version_	1718443186079137792

NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

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