1. Deep learning for mass detection in Full Field Digital Mammograms
- Author
-
Oliver Diaz, Richa Agarwal, Moi Hoon Yap, Xavier Lladó, Robert Martí, and Ministerio de Economía y Competitividad (Espanya)
- Subjects
0301 basic medicine ,Scanner ,Computer science ,Health Informatics ,Breast Neoplasms ,Convolutional neural network ,Càncer de mama ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Breast cancer ,Medical imaging ,medicine ,Mammography ,Humans ,Diagnosis, Computer-Assisted ,Breast -- Radiography ,Mama -- Càncer -- Imatgeria ,Early Detection of Cancer ,Breast -- Cancer -- Imaging ,medicine.diagnostic_test ,business.industry ,Deep learning ,Pattern recognition ,Mama -- Radiografia ,Full field ,Mamografia ,Computer Science Applications ,030104 developmental biology ,Benchmark (computing) ,Imatgeria mèdica ,Female ,Artificial intelligence ,Neural Networks, Computer ,business ,Transfer of learning ,030217 neurology & neurosurgery ,Imaging systems in medicine - 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
- Published
- 2020