Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112018
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dc.contributor.authorCardoso, J.en
dc.contributor.authorMarques, N.en
dc.contributor.authorDhungel, N.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorBradley, A.en
dc.date.issued2018en
dc.identifier.citationProceedings of the 24th IEEE International Conference on Image Processing (ICIP 2017), 2018 / vol.2017, pp.1737-1741en
dc.identifier.isbn9781509021758en
dc.identifier.issn1522-4880en
dc.identifier.urihttp://hdl.handle.net/2440/112018-
dc.description.abstractThrough the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in mammograms. In these CAD systems, mass segmentation plays a central role in the decision process. In the literature, mass segmentation has been typically evaluated in a intra-sensor scenario, where the methodology is designed and evaluated in similar data. However, in practice, acquisition systems and PACS from multiple vendors abound and current works fails to take into account the differences in mammogram data in the performance evaluation. In this work it is argued that a comprehensive assessment of the mass segmentation methods requires the design and evaluation in datasets with different properties. To provide a more realistic evaluation, this work proposes: a) improvements to a state of the art method based on tailored features and a graph model; b) a head-to-head comparison of the improved model with recently proposed methodologies based in deep learning and structured prediction on four reference databases, performing a cross-sensor evaluation. The results obtained support the assertion that the evaluation methods from the literature are optimistically biased when evaluated on data gathered from exactly the same sensor and/or acquisition protocol.en
dc.description.statementofresponsibilityJaime S. Cardoso, Nuno Marques, Neeraj Dhungel, G. Carneiro, A. P. Bradleyen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Conference on Image Processing ICIPen
dc.rights©2017 IEEEen
dc.source.urihttps://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8267582en
dc.subjectMammogram; mass segmentation; transfer learning; cross-sensoren
dc.titleMass segmentation in mammograms: a cross-sensor comparison of deep and tailored featuresen
dc.typeConference paperen
dc.identifier.rmid0030085935en
dc.contributor.conference24th IEEE International Conference on Image Processing (ICIP 2017) (17 Sep 2017 - 20 Sep 2017 : Beijing, CHINA)en
dc.identifier.doi10.1109/ICIP.2017.8296579en
dc.publisher.placePiscataway, N.J.en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794en
dc.identifier.pubid406443-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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