  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 F1-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.
