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Type: Journal article
Title: Automated analysis of unregistered multi-view mammograms with deep learning
Author: Carneiro, G.
Nascimento, J.
Bradley, A.
Citation: IEEE Transactions on Medical Imaging, 2017; 36(11):2355-2365
Publisher: IEEE
Issue Date: 2017
ISSN: 0278-0062
Statement of
Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley
Abstract: We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e., masses and micro-calcifications). This is a holistic methodology that can classify a whole mammographic exam, containing the CC and MLO views and the segmentation maps, as opposed to the classification of individual lesions, which is the dominant approach in the field. We also demonstrate that the proposed system is capable of using the segmentation maps generated by automated mass and micro-calcification detection systems, and still producing accurate results. The semi-automated approach (using manually defined mass and micro-calcification segmentation maps) is tested on two publicly available data sets (INbreast and DDSM), and results show that the volume under ROC surface (VUS) for a 3-class problem (normal tissue, benign, and malignant) is over 0.9, the area under ROC curve (AUC) for the 2-class "benign versus malignant" problem is over 0.9, and for the 2-class breast screening problem (malignancy versus normal/benign) is also over 0.9. For the fully automated approach, the VUS results on INbreast is over 0.7, and the AUC for the 2-class "benign versus malignant" problem is over 0.78, and the AUC for the 2-class breast screening is 0.86.
Keywords: Deep learning; mammogram; multi-view classification; transfer learning
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
RMID: 0030076828
DOI: 10.1109/TMI.2017.2751523
Grant ID:
Appears in Collections:Computer Science publications

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