  The paper presents and comparatively analyses several deep learning
approaches to automatically detect tuberculosis related lesions in lung CTs, in
the context of the ImageClef 2020 Tuberculosis task. Three classes of methods,
different with respect to the way the volumetric data is given as input to
neural network-based classifiers are discussed and evaluated. All these come
with a rich experimental analysis comprising a variety of neural network
architectures, various segmentation algorithms and data augmentation schemes.
The reported work belongs to the SenticLab.UAIC team, which obtained the best
results in the competition.
