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Deep learning for mass detection in Full Field Digital Mammograms

Deep learning for mass detection in Full Field Digital Mammograms

Authors :
Oliver Diaz
Richa Agarwal
Moi Hoon Yap
Xavier Lladó
Robert Martí
Ministerio de Economía y Competitividad (Espanya)
Source :
Computers in Biology and Medicine, 2020, vol. 121, art. núm.103774, Articles publicats (D-ATC), DUGiDocs – Universitat de Girona, instname, Dipòsit Digital de la UB, Universidad de Barcelona
Publication Year :
2020
Publisher :
Elsevier, 2020.

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 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

Details

Database :
OpenAIRE
Journal :
Computers in Biology and Medicine, 2020, vol. 121, art. núm.103774, Articles publicats (D-ATC), DUGiDocs – Universitat de Girona, instname, Dipòsit Digital de la UB, Universidad de Barcelona
Accession number :
edsair.doi.dedup.....c390fe628441236758c4daa065853078