Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compbiomed.2020.103774 | Computers in Biology and Medicine |
Keywords | DocType | Volume |
Deep learning,CNN,Mammogram,FFDM,Mass detection | Journal | 121 |
ISSN | Citations | PageRank |
0010-4825 | 1 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richa Agarwal | 1 | 1 | 0.37 |
| Oliver Díaz | 2 | 1 | 0.37 |
| Moi Hoon Yap | 3 | 190 | 27.82 |
| Xavier Lladó | 4 | 1 | 0.37 |
| Robert Martí | 5 | 1 | 0.37 |