  In the last two decades Computer Aided Diagnostics (CAD) systems were
developed to help radiologists analyze screening mammograms. The benefits of
current CAD technologies appear to be contradictory and 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 the 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 availaible
online at https://github.com/riblidezso/frcnn_cad .
