Automatic mass detection in mammograms using deep convolutional neural networks
dc.contributor.author
dc.date.accessioned
2020-06-19T07:41:11Z
dc.date.available
2020-06-19T07:41:11Z
dc.date.issued
2019-02-20
dc.identifier.uri
dc.description.abstract
With recent advances in the field of deep learning, the use of convolutional neural networks (CNNs) in medical imaging has become very encouraging. The aim of our paper is to propose a patch-based CNN method for automated mass detection in full-field digital mammograms (FFDM). In addition to evaluating CNNs pretrained with the ImageNet dataset, we investigate the use of transfer learning for a particular domain adaptation. First, the CNN is trained using a large public database of digitized mammograms (CBIS-DDSM dataset), and then the model is transferred and tested onto the smaller database of digital mammograms (INbreast dataset). We evaluate three widely used CNNs (VGG16, ResNet50, InceptionV3) and show that the InceptionV3 obtains the best performance for classifying the mass and nonmass breast region for CBIS-DDSM. We further show the benefit of domain adaptation between the CBIS-DDSM (digitized) and INbreast (digital) datasets using the InceptionV3 CNN. Mass detection evaluation follows a fivefold cross-validation strategy using free-response operating characteristic curves. Results show that the transfer learning from CBIS-DDSM obtains a substantially higher performance with the best true positive rate (TPR) of 0.98 ± 0.02 at 1.67 false positives per image (FPI), compared with transfer learning from ImageNet with TPR of 0.91 ± 0.07 at 2.1 FPI. In addition, the proposed framework improves upon mass detection results described in the literature on the INbreast database, in terms of both TPR and FPI
dc.description.sponsorship
This work is partially supported by SMARTER project funded
by Ministry of Economy and Competitiveness of Spain, under
project reference DPI2015-68442-R.A. is funded by the support
of the Secretariat of Universities and Research, Ministry of
Economy and Knowledge, Government of Catalonia Ref.
ECO/1794/2015 FIDGR-2016
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Society of Photo-optical Instrumentation Engineers
dc.relation
info:eu-repo/grantAgreement/MINECO//DPI2015-68442-R/ES/ANALISIS DE IMAGENES INTELIGENTE PARA LOS RETOS EN EL CRIBADO DE CANCER DE MAMA/
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1117/1.JMI.6.3.031409
dc.relation.ispartof
Journal of Medical Imaging, 2019, vol. 6, núm. 3, p. 031409
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Automatic mass detection in mammograms using deep convolutional neural networks
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
030013
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.ProjectAcronym
dc.identifier.eissn
2329-4310