BatmanLab in the ImageCLEF tuberculosis task 2017

Dicente Cid, Yashin (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Batmanghelich, Kayhan (University of Pittsburgh, USA)

In this work we present our participation in the ImageCLEF 2017 tuberculosis task. The task consists of detecting five tuberculosis (TB) types and predicting drug resistance from lung CT (Computed Tomography) volumes. Our approach is based on a previously developed non-parametric method. Tested on CT images of Chronic Obstructive Pulmonary Disease (COPD) patients, it consists of describing each subject as a collection of local feature descriptors embedded in a dissimilarity space. The set of local features was extended for this work adding new 3D texture descriptors. The results shows that our approach is able to characterize several TB types, achieving a Cohen’s Kappa coefficient of 0.1533, but does not suit for predicting drug resistance were it only achieved an AUC of 0.5241.


Mots-clés:
Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Dublin, Ireland, 11-14 September 2017
Date:
Dublin, Ireland
11-14 September 2017
2017
Pagination:
7 p.
Publié dans
Proceedings of the CLEF 2017 Working Notes
ISSN:
1613-0073
Ressource(s) externe(s):
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 Notice créée le 2017-12-20, modifiée le 2018-08-31

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