Breast cancer represents one of the the most common forms of cancer today, and diagnosing it early is pivotal to reduce mortality. In early cancer prevention programs, women undergo biennial exams in which up to four, high-resolution images are acquired: these four views are acquired through two different projections, namely bilateral craniocaudal (CC) and mediolateral oblique (MLO) views, and for each side of the breast. These exams have to be reviewed by radiologists: however, due to the huge amount of women that undergo screening mammography, doctors are often supported by computer aided detection systems (CAD) to hasten this process. Deep learning-based techniques could be used for the automated screening of digital mammography images in order to identify cases with high probability of being normal, and secondarily identify lesions.