In the last two decades, Computer Aided Detection (CAD) systems were developed to
help radiologists analyse screening mammograms, however benefits of current CAD technologies
appear to be contradictory, therefore they should be improved to be ultimately considered
useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous
success in image recognition, reaching human performance. These methods have greatly
surpassed the traditional approaches, which are similar to currently used CAD solutions.
Deep CNN-s have the potential to revolutionize medical image analysis. We propose
a CAD system based on one of the most successful object detection frameworks, Faster
R-CNN. The system detects and classifies malignant or benign lesions on a mammogram
without any human intervention. The proposed method sets the state of the art classification
performance on the public INbreast database, AUC = 0.95. The approach described here
has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85.
When used as a detector, the system reaches high sensitivity with very few false positive
marks per image on the INbreast dataset. Source code, the trained model and an OsiriX
plugin are published online at https://github.com/riblidezso/frcnn_cad .
