Automatic identification of abnormalities is a key problem in medical imaging. While the majority of previous work in mammography has focused on classification of abnormalities rather than detection and localization, here we introduce a novel deep learning method for detection of masses and calcifications. The power of this approach comes from generating an ensemble of individual Faster-RCNN models each trained for a specific set of abnormal clinical categories, together with extending a modified two stage Faster-RCNN scheme to a three stage cascade. The third stage being an additional classifier working directly on the image pixels with the handful of sub-windows generated by the first two stages. The performance of the algorithm is evaluated on the INBreast benchmark and on a large internal multi-center dataset. Quantitative results compete well with state of the art in terms of accuracy. Computationally the methods runs significantly faster than current state-of-the art techniques.
CITATION STYLE
Akselrod-Ballin, A., Karlinsky, L., Hazan, A., Bakalo, R., Horesh, A. B., Shoshan, Y., & Barkan, E. (2017). Deep learning for automatic detection of abnormal findings in breast mammography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10553 LNCS, pp. 321–329). Springer Verlag. https://doi.org/10.1007/978-3-319-67558-9_37
Mendeley helps you to discover research relevant for your work.