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...
| Authors: | ; ; ; ; |
|---|---|
| Format: | Article |
| Status: | Published version |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/18496 |
| Online Access: | http://hdl.handle.net/10256/18496 |
| Access Level: | Open access |
| Keyword: | Mama -- Càncer -- Imatgeria Breast -- Cancer -- Imaging Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography |
| _version_ | 1878742506838949888 |
|---|---|
| author | Agarwal, Richa |
| author2 | Diaz Montesdeoca, Oliver Yap, Moi Hoon Lladó Bardera, Xavier Martí Marly, Robert |
| author2_role | author author author author |
| author_browse | Agarwal, Richa Diaz Montesdeoca, Oliver Lladó Bardera, Xavier Martí Marly, Robert Yap, Moi Hoon |
| author_facet | Agarwal, Richa Diaz Montesdeoca, Oliver Yap, Moi Hoon Lladó Bardera, Xavier Martí Marly, Robert |
| author_role | author |
| collection | Recercat. Dipósit de la Recerca de Catalunya |
| 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 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_f72c3e4e9a370dae63e2527d2f8bd8df |
| institution | 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) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:recercat.cat:10256/18496 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | Recercat. Dipósit de la Recerca de Catalunya |
| spelling | Deep learning for mass detection in Full Field Digital Mammograms Agarwal, Richa Diaz Montesdeoca, Oliver Yap, Moi Hoon Lladó Bardera, Xavier Martí Marly, Robert Mama -- Càncer -- Imatgeria Breast -- Cancer -- Imaging Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography 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 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 Elsevier http://hdl.handle.net/10256/18496 |
| 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 |
| status_str | publishedVersion |
| title | 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_short | Deep learning for mass detection in Full Field Digital Mammograms |
| title_sort | Deep learning for mass detection in Full Field Digital Mammograms |
| topic | Mama -- Càncer -- Imatgeria Breast -- Cancer -- Imaging Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography |
| topic_facet | Mama -- Càncer -- Imatgeria Breast -- Cancer -- Imaging Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography |
| url | http://hdl.handle.net/10256/18496 |