Technical report on the development and interpretation of convolutional neural networks for the classification of multiparametric MRI images on unbalanced datasets. Case study: prostate cancer

This report summarized the activities carried out to define, train and validate Deep Learning models for the classification of medical imaging data. The issue of unbalanced datasets was faced by applying some data augmentation techniques, based on transformation of the original images. Such techniques were compared to verify their impact in a frame where object morphology is relevant. Multimodal deep learning models were defined to exploit the information contained in heterogeneous imaging data and cope with data distribution imbalance. To verify the inner functioning of the deep learning models, the LIME algorithm was applied, thus checking that the regions that contribute to the classification were the real meaningful ones. The case study used to was the categorization of prostate cancer aggressiveness based on Magnetic Resonance Imaging (MRI) data. The aggressiveness was determined, as a ground truth, via tissue biopsy and expressed with a score from 2 to 10 known as Gleason Score, which is obtained as the sum of two values, each one from 1 to 5, associated with the two most common patterns in the tumor tissue histological sample.