The thesis describes an analysis on the application of a novel paradigm in computer vision named Logic Tensor Network (LTN), a branch of Neuro-Symbolic AI, that aims to combine the strengths of symbolic reasoning, provided by First Order Logic (FOL), and Neural Networks (NN). The strategy adopted is based on a patch-level deep learning framework capable of identifying the most common mammographic signs of lesions: microcalcifications and masses, by analyzing patches extracted from the four standard views used in screening mammography: R-CC (Right Craniocaudal), L-CC (Left Craniocaudal), R-MLO (Right Mediolateral Oblique) and L- MLO (Left Mediolateral Oblique). The goal is to predict the status of each individual patch in one of three possible outcomes: Normal - no cancer; Benign - a benign finding; Malignant - dangerous cancer.