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Purpose: In this paper, for the purpose of accurate and efficient mass detection, we propose a new deep learning framework, including two major stages: Suspicious region localization (SRL) and Multicontext Multitask Learning (MCMTL).

Methods: In the first stage, SRL focuses on finding suspicious regions [regions of interest (ROIs)] and extracting multisize patches of these suspicious regions. A set of bounding boxes with different size is used to extract multisize patches, which aim to capture diverse context information. In the second stage, MCMTL networks integrate features from multisize patches of suspicious regions for classification and segmentation simultaneously, where the purpose of this stage is to keep the true positive suspicious regions and to reduce the false positive suspicious regions.

Results: According to the experimental results on two public datasets (i.e., CBIS-DDSM and INBreast), our method achieves the overall performance of 0.812 TPR@2.53 FPI and 0.919 TPR@0.12 FPI on test sets, respectively.

Conclusions: Our proposed method suggests comparable performance to the state-of-the-art methods.

Titel:	Multicontext multitask learning networks for mass detection in mammogram.
Autor/in / Beteiligte Person:	Shen, R ; Zhou, K ; Yan, K ; Tian, K ; Zhang, J
Link:	Full Text Finder (Volltext)
Zeitschrift:	Medical physics, Jg. 47 (2020-04-01), Heft 4, S. 1566-1578
Veröffentlichung:	2017- : Hoboken, NJ : John Wiley and Sons, Inc. ; <i>Original Publication</i>: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics., 2020
Medientyp:	academicJournal
ISSN:	2473-4209 (electronic)
DOI:	10.1002/mp.13945
Schlagwort:	Breast Neoplasms diagnostic imaging Image Processing, Computer-Assisted methods Machine Learning Mammography
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Med Phys] 2020 Apr; Vol. 47 (4), pp. 1566-1578. <i>Date of Electronic Publication: </i>2020 Mar 05. MeSH Terms: Machine Learning* ; Mammography* ; Breast Neoplasms / diagnostic imaging ; Image Processing, Computer-Assisted / methods References: World Health Organization. Breast cancer; 2017. ; Stewart B, Wild CP. World cancer report 2014. Health; 2017. ; Oliver A, Freixenet J, Marti J, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal. 2010;14:87-110. ; Bionetworks S. Digital Mammography DREAM. Challenge. 2017. ; Shen L. End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design, arXiv preprint arXiv:1708.09427, 2017. ; Dhungel N, Carneiro G, Bradley AP. Automated mass detection in mammograms using cascaded deep learning and random forests. 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Breast mass detection from the digitized x-ray mammograms based on the combination of deep active learning and self-paced learning. Future Gener Comp Syst. 2019;101:668-679. Contributed Indexing: Keywords: deep learning; mass detection; multi-context learning; multi-task learning Entry Date(s): Date Created: 20191205 Date Completed: 20210125 Latest Revision: 20210125 Update Code: 20231215

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Multicontext multitask learning networks for mass detection in mammogram.