  State-of-the-art deep learning methods for image processing are evolving into
increasingly complex meta-architectures with a growing number of modules. Among
them, region-based fully convolutional networks (R-FCN) and deformable
convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for
speed and low consumption of memory, which is crucial for processing the high
resolutions of to 50 micrometers used by radiologists. Deformable convolution
and pooling can model a wide range of mammographic findings of different
morphology and scales, thanks to their versatility. In this study, we present a
neural net architecture based on R-FCN / DCN, that we have adapted from the
natural image domain to suit mammograms -- particularly their larger image size
-- without compromising resolution. We trained the network on a large, recently
released dataset (Optimam) including 6,500 cancerous mammograms. By combining
our modern architecture with such a rich dataset, we achieved an area under the
ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000
withheld images), which surpassed all other submissions in the competitive
phase.
