Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111345
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dc.contributor.authorDhungel, N.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorBradley, A.en
dc.date.issued2017en
dc.identifier.citationProceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017 / pp.310-314en
dc.identifier.isbn9781509011711en
dc.identifier.issn1945-7928en
dc.identifier.issn1945-8452en
dc.identifier.urihttp://hdl.handle.net/2440/111345-
dc.description.abstractIn this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the automatically produced binary segmentation maps of the masses and micro-calcifications in each view. We then form the mResNet by concatenating the outputs of each ResNet at the second to last layer, followed by a final, fully connected, layer. The resulting mResNet is trained in an end-to-end fashion to produce a case-based mammogram classifier that has the potential to be used in breast screening programs. We empirically show on the publicly available INbreast dataset, that the proposed mResNet classifies mammograms into malignant or normal/benign with an AUC of 0.8.en
dc.description.statementofresponsibilityNeeraj Dhungel, Gustavo Carneiro, Andrew P. Bradleyen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Symposium on Biomedical Imagingen
dc.rights©2017 IEEEen
dc.source.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7944115en
dc.subjectMammogram; classification; multi-view; residual neural networken
dc.titleFully automated classification of mammograms using deep residual neural networksen
dc.typeConference paperen
dc.identifier.rmid0030073408en
dc.contributor.conferenceIEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)en
dc.identifier.doi10.1109/ISBI.2017.7950526en
dc.publisher.placeOnlineen
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794en
dc.identifier.pubid365542-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Computer Science publications

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