Keywords: Self-Normalizing Neural Networks, multi-task, homoscedastic uncertainty
TL;DR: Self-Normalizing Neural Networks, 3D-CNNs and Multi-task loss guided by uncertainty for Tuberculosis
Abstract: We propose a learning method well-suited to infer the presence of Tuberculosis (TB) manifestations on Computer Tomography (CT) scans mimicking the radiologist reports. Latent features are extracted from the CT volumes employing the V-Net encoder and those are the input to a Feed-Forward Neural Network (FNN) for multi-class classification. To overtake the issues (e.g., exploding/vanishing gradients, lack of sensibility) that normally appear when training deep 3D models with datasets of limited size and composed of large volumes, our proposal employs: 1) At the network architecture level, the scaled exponential linear unit (SELU) activation which allows the network self-normalization, and 2) at the learning phase, multi-task learning with a loss function weighted by the task homoscedastic uncertainty. The results achieve F 1 -scores close to or above 0.9 for the detection of TB lesions and a Root Mean Square Error of 1.16 for the number of nodules.
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