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DC Field | Value | Language |
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dc.contributor.author | Agarwal, Richa | - |
dc.contributor.author | Díaz, Oliver | - |
dc.contributor.author | Yap, Moi Hoon | - |
dc.contributor.author | Llado, Xavier | - |
dc.contributor.author | Marti, Robert | - |
dc.date.accessioned | 2021-04-26T12:54:55Z | - |
dc.date.available | 2021-04-26T12:54:55Z | - |
dc.date.issued | 2020-06-01 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.uri | http://hdl.handle.net/2445/176697 | - |
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.format.extent | 10 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier Ltd | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1016/j.compbiomed.2020.103774 | - |
dc.relation.ispartof | Computers in Biology and Medicine, 2020, vol. 121 | - |
dc.relation.uri | https://doi.org/10.1016/j.compbiomed.2020.103774 | - |
dc.rights | cc by-nc-nd (c) Agarwal, 2020 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es | - |
dc.source | Articles publicats en revistes (Matemàtiques i Informàtica) | - |
dc.subject.classification | Càncer de mama | - |
dc.subject.classification | Mamografia | - |
dc.subject.other | Breast cancer | - |
dc.subject.other | Mammography | - |
dc.title | Deep learning for mass detection in Full Field Digital Mammograms | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 708326 | - |
dc.date.updated | 2021-04-26T12:54:55Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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708326.pdf | 2.28 MB | Adobe PDF | View/Open |
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