A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

Abstract The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identificati...

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Autores principales:	Anabia Sohail, Asifullah Khan, Noorul Wahab, Aneela Zameer, Saranjam Khan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0e4a3931d80c41a0a127dde5e87580bd
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spelling	oai:doaj.org-article:0e4a3931d80c41a0a127dde5e87580bd2021-12-02T17:04:59ZA multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images10.1038/s41598-021-85652-12045-2322https://doaj.org/article/0e4a3931d80c41a0a127dde5e87580bd2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85652-1https://doaj.org/toc/2045-2322Abstract The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.Anabia SohailAsifullah KhanNoorul WahabAneela ZameerSaranjam KhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anabia Sohail Asifullah Khan Noorul Wahab Aneela Zameer Saranjam Khan A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
description	Abstract The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.
format	article
author	Anabia Sohail Asifullah Khan Noorul Wahab Aneela Zameer Saranjam Khan
author_facet	Anabia Sohail Asifullah Khan Noorul Wahab Aneela Zameer Saranjam Khan
author_sort	Anabia Sohail
title	A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
title_short	A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
title_full	A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
title_fullStr	A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
title_full_unstemmed	A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images
title_sort	multi-phase deep cnn based mitosis detection framework for breast cancer histopathological images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0e4a3931d80c41a0a127dde5e87580bd
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_version_	1718381773693386752

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

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