# A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations

arXiv: https://arxiv.org/abs/1907.12331 · HTML: https://mlsanity.com/paper/1907.12331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Translational Lung Imaging Analysis Through Disentangled Representations](https://mlsanity.com/paper/2203.01668/) (2022)

## Abstract (arXiv)

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.