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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doaj-0e4a3931d80c41a0a127dde5e87580bd2021-03-21T12:36:24ZengNature Publishing GroupScientific Reports2045-23222021-03-0111111810.1038/s41598-021-85652-1A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological imagesAnabia Sohail0Asifullah Khan1Noorul Wahab2Aneela Zameer3Saranjam Khan4Pattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS)Pattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS)Department of Computer Science, Tissue Image Analytics (TIA) Lab, University of WarwickPattern Recognition Lab, DCIS, Pakistan Institute of Engineering and Applied Sciences (PIEAS)Department of Physics, Islamia College PeshawarAbstract 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.https://doi.org/10.1038/s41598-021-85652-1 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anabia Sohail Asifullah Khan Noorul Wahab Aneela Zameer Saranjam Khan |
spellingShingle |
Anabia Sohail Asifullah Khan Noorul Wahab Aneela Zameer Saranjam Khan A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-03-01 |
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. |
url |
https://doi.org/10.1038/s41598-021-85652-1 |
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