Nov 11, 2020 – 11:00 – 11:45 AM
Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to an extra dose of radiation. In this paper, we propose a new DL model, based on a Siamese neural network, able to withstand random noise perturbations. We use data coming from the ProstateX challenge and demonstrate the superior robustness of our model to random noise compared to a similar architecture, albeit deprived of the Siamese branch. In addition, our approach is also resistant to adversarial attacks and shows overall better AUC performance.