Background: The aim of this thesis is to explore the solutions that Deep Learning techniques can offer in the field of Medical Imaging, in particular for breast cancer characterisation in magnetic resonance images. The thesis proposes the development of a Deep Learning architecture for a concrete problem such as the evaluation of pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. Methods: The mpMRI dataset analysed includes 37 patients, each of whom underwent two studies: before and after 2 cycles of NAC. An index slice was extracted from each available sequence by an experienced radiologist. Pathological results were used as ground truth.