One of the main difficulties in the use of deep learning strategies in medical contexts is the training set size. While these methods need large annotated training sets, this data is costly to obtain in medical contexts and suffers from intra and iter-subject variability.

In the present work, two new pre-processing techniques are introduced to improve a classifier performance. First, data augmentation based on co-registration is suggested. Then, multi-scale enhancement based on Difference of Gaussians is proposed.

Results are accessed in a public mammogram database, the InBreast, in the context of an ordinal problem, the BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network with the AlexNet architecture was used as a base classifier.

The multi-class classification experiments show that the proposed pipeline with the Difference of Gaussians and the data augmentation technique outperforms using the original dataset only and using the original dataset augmented by mirroring the images.