Deep learning for mass detection in Full Field Digital Mammograms

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 Mammog...

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Detalles Bibliográficos
Autores: Agarwal, Richa, Diaz Montesdeoca, Oliver, Yap, Moi Hoon, Lladó Bardera, Xavier, Martí Marly, Robert
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/18496
Acceso en línea:http://hdl.handle.net/10256/18496
Access Level:acceso abierto
Palabra clave:Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Imatgeria mèdica
Imaging systems in medicine
Mama -- Radiografia
Breast -- Radiography
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repository_id_str
spelling Deep learning for mass detection in Full Field Digital MammogramsAgarwal, RichaDiaz Montesdeoca, OliverYap, Moi HoonLladó Bardera, XavierMartí Marly, RobertMama -- Càncer -- ImatgeriaBreast -- Cancer -- ImagingImatgeria mèdicaImaging systems in medicineMama -- RadiografiaBreast -- RadiographyIn 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 screeningThis 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-2016ElsevierMinisterio de Economía y Competitividad (Espanya)2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/18496http://hdl.handle.net/10256/18496Computers in Biology and Medicine, 2020, vol. 121, art. núm.103774Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2020.103774info:eu-repo/semantics/altIdentifier/issn/0010-4825info:eu-repo/grantAgreement/MINECO//DPI2015-68442-RAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/184962026-05-29T05:05:01Z
dc.title.none.fl_str_mv Deep learning for mass detection in Full Field Digital Mammograms
title Deep learning for mass detection in Full Field Digital Mammograms
spellingShingle Deep learning for mass detection in Full Field Digital Mammograms
Agarwal, Richa
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Imatgeria mèdica
Imaging systems in medicine
Mama -- Radiografia
Breast -- Radiography
title_short Deep learning for mass detection in Full Field Digital Mammograms
title_full Deep learning for mass detection in Full Field Digital Mammograms
title_fullStr Deep learning for mass detection in Full Field Digital Mammograms
title_full_unstemmed Deep learning for mass detection in Full Field Digital Mammograms
title_sort Deep learning for mass detection in Full Field Digital Mammograms
dc.creator.none.fl_str_mv Agarwal, Richa
Diaz Montesdeoca, Oliver
Yap, Moi Hoon
Lladó Bardera, Xavier
Martí Marly, Robert
author Agarwal, Richa
author_facet Agarwal, Richa
Diaz Montesdeoca, Oliver
Yap, Moi Hoon
Lladó Bardera, Xavier
Martí Marly, Robert
author_role author
author2 Diaz Montesdeoca, Oliver
Yap, Moi Hoon
Lladó Bardera, Xavier
Martí Marly, Robert
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (Espanya)
dc.subject.none.fl_str_mv Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Imatgeria mèdica
Imaging systems in medicine
Mama -- Radiografia
Breast -- Radiography
topic Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Imatgeria mèdica
Imaging systems in medicine
Mama -- Radiografia
Breast -- Radiography
description 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
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/18496
http://hdl.handle.net/10256/18496
url http://hdl.handle.net/10256/18496
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2020.103774
info:eu-repo/semantics/altIdentifier/issn/0010-4825
info:eu-repo/grantAgreement/MINECO//DPI2015-68442-R
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Computers in Biology and Medicine, 2020, vol. 121, art. núm.103774
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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