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2020-06-01

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cc by-nc-nd (c) Agarwal, 2020
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/176697

Deep learning for mass detection in Full Field Digital Mammograms

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Recurs relacionat

https://doi.org/10.1016/j.compbiomed.2020.103774

Resum

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.

Matèries

Càncer de mama, Mamografia

Matèries (anglès)

Breast cancer, Mammography

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Articles publicats en revistes (Matemàtiques i Informàtica)

Citació

AGARWAL, Richa, et al. Deep learning for mass detection in Full Field Digital Mammograms. Computers in Biology and Medicine. 2020. Vol. 121. ISSN 0010-4825. [consulted: 8 of August of 2026]. Available at: https://hdl.handle.net/2445/176697

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