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http://hdl.handle.net/2445/176697
Title: | Deep learning for mass detection in Full Field Digital Mammograms |
Author: | Agarwal, Richa Díaz, Oliver Yap, Moi Hoon Llado, Xavier Marti, Robert |
Keywords: | Càncer de mama Mamografia Breast cancer Mammography |
Issue Date: | 1-Jun-2020 |
Publisher: | Elsevier Ltd |
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. |
Note: | Reproducció del document publicat a: https://doi.org/10.1016/j.compbiomed.2020.103774 |
It is part of: | Computers in Biology and Medicine, 2020, vol. 121 |
URI: | http://hdl.handle.net/2445/176697 |
Related resource: | https://doi.org/10.1016/j.compbiomed.2020.103774 |
ISSN: | 0010-4825 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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