Background: Breast cancer is the second most common cause of death from cancer in women in the United States after lung cancer. Thanks to early detection and treatment improvements, the mortality rate has been steadily decreasing in the last decades. Therefore, there is an increasing interest in finding new methodologies for improving the current state of the art. Several works validated the efficiency of Artificial Intelligence (AI) algorithms for cancer detection and diagnosis, but the application of uncertainty-based models, which have potential to enhance result interpretability and therefore clinical translation, remains to be investigated in depth. Project: This thesis aims to develop and validate Deep Learning algorithms for tumor classification and segmentation on 3D contrast-enhanced breast computed tomographic (CE-BCT) scans, exploring the mass-level uncertainty of the predictions through the Monte Carlo Dropout.