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Lesion classification in mammograms using convolutional neural networks and transfer learning

dc.contributor.authorPerre, Ana
dc.contributor.authorAlexandre, Luís A.
dc.contributor.authorFreire, Luís
dc.date.accessioned2017-12-26T23:30:50Z
dc.date.available2017-12-26T23:30:50Z
dc.date.issued2018
dc.description.abstractComputer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early-stage cancers, decreasing the false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) is one example of deep learning algorithms that proved to be successful in image classification. In this paper, we aim to study the application of CNN's to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, which is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNN's and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPerre A, Alexandre LA, Freire L. Lesion classification in mammograms using convolutional neural networks and transfer learning. In: Tavares J, Natal Jorge R, editors. VipIMAGE 2017 - ECCOMAS 2017: lecture notes in computational vision and biomechanics (Vol. 27). Cham: Springer; 2018. p. 360-8.pt_PT
dc.identifier.doi10.1007/978-3-319-68195-5_40pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/7802
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-319-68195-5_40pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectComputer-aided detectionpt_PT
dc.subjectConvolutional neural networkspt_PT
dc.subjectMammographypt_PT
dc.titleLesion classification in mammograms using convolutional neural networks and transfer learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage368pt_PT
oaire.citation.startPage360pt_PT
oaire.citation.volume27pt_PT
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT

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