Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110578
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCarneiro, G.en
dc.contributor.authorNascimento, J.en
dc.contributor.authorBradley, A.en
dc.date.issued2017en
dc.identifier.citationIEEE Transactions on Medical Imaging, 2017; 36(11):2355-2365en
dc.identifier.issn0278-0062en
dc.identifier.issn1558-254Xen
dc.identifier.urihttp://hdl.handle.net/2440/110578-
dc.description.abstractWe 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.en
dc.description.statementofresponsibilityGustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradleyen
dc.language.isoenen
dc.publisherIEEEen
dc.rights© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en
dc.subjectDeep learning; mammogram; multi-view classification; transfer learningen
dc.titleAutomated analysis of unregistered multi-view mammograms with deep learningen
dc.typeJournal articleen
dc.identifier.rmid0030076828en
dc.identifier.doi10.1109/TMI.2017.2751523en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794en
dc.relation.granthttp://purl.org/au-research/grants/arc/FT110100623en
dc.identifier.pubid372226-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS14en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.