End-to-end metastasis detection of breast cancer from histopathology whole slide images.

Khaliliboroujeni, S; He, X; Jia, W; Amirgholipour, S

End-to-end metastasis detection of breast cancer from histopathology whole slide images.

Khaliliboroujeni, S He, X Jia, W

Amirgholipour, S

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Comput Med Imaging Graph, 2022, 102, pp. 102136
Issue Date:: 2022-10-14

Closed Access

	Filename	Description	Size
	20220715_CMIG_r1_Sepi.pdf	Accepted version	720.53 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Khaliliboroujeni, S
dc.contributor.author	He, X
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	Amirgholipour, S
dc.date.accessioned	2022-11-30T02:46:33Z
dc.date.available	2022-10-07
dc.date.available	2022-11-30T02:46:33Z
dc.date.issued	2022-10-14
dc.identifier.citation	Comput Med Imaging Graph, 2022, 102, pp. 102136
dc.identifier.issn	0895-6111
dc.identifier.issn	1879-0771
dc.identifier.uri	http://hdl.handle.net/10453/163876
dc.description.abstract	Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Comput Med Imaging Graph
dc.relation.isbasedon	10.1016/j.compmedimag.2022.102136
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 1103 Clinical Sciences
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.title	End-to-end metastasis detection of breast cancer from histopathology whole slide images.
dc.type	Journal Article
utslib.citation.volume	102
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	1103 Clinical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-11-30T02:46:31Z
pubs.publication-status	Published online
pubs.volume	102

Abstract:

Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/163876