Deep learning for mass detection in Full Field Digital Mammograms
dc.contributor.author
dc.date.accessioned
2020-10-05T14:43:27Z
dc.date.available
2020-10-05T14:43:27Z
dc.date.issued
2020-06
dc.identifier.issn
0010-4825
dc.identifier.uri
dc.description.abstract
In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of 80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening
dc.description.sponsorship
This work is partially supported by SMARTER project funded by
the Ministry of Economy and Competitiveness of Spain, under project
reference DPI2015-68442-R, and the ICEBERG project (Ref. RTI2018-
096333-B-I00) funded by the Ministry of Science, Innovation and Universities. R. Agarwal 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
Elsevier
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/10.1016/j.compbiomed.2020.103774
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Computers in Biology and Medicine, 2020, vol. 121, art. núm.103774
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Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.subject
dc.title
Deep learning for mass detection in Full Field Digital Mammograms
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
031648
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
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