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Résumé

Tuberculosis (TB) remains a leading cause of death worldwide. Two main challenges when assessing computed tomography scans of TB patients are detecting multi{drug resistance and di_erentiating TB types. In this article we model the lungs as a graph entity where nodes represent anatomical lung regions and edges encode interactions between them. This graph is able to characterize the texture distribution along the lungs, making it suitable for describing patients with different TB types. In 2017, the ImageCLEF benchmark proposed a task based on computed tomography volumes of patients with TB. This task was divided into two subtasks: multi{drug resistance prediction, and TB type classi_cation. The participation in this task showed the strength of our model, leading to best results in the competition for multi{drug resistance detection (AUC = 0.5825) and good results in the TB type classi_cation (Cohen's Kappa coe_cient = 0.1623).

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