Overview of ImageCLEFtuberculosis 2018 ::detecting multi-drug resistance, classifying tuberculosis types and assessing severity scores

Abstract

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language-independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well as the retrieval of images. The tuberculosis task was held for the first time in 2017 and had a very encouraging participation with 9 groups submitting results to these very challenging tasks. In 2018 there was a slightly higher participation. Three tasks were proposed in 2018: (1) the detection of drug resistances among tuberculosis cases, (2) the classification of the cases into five types of tuberculosis and (3) the assessment of a tuberculosis severity score. Many different techniques were used by the participants ranging from Deep Learning to graph-based approaches and best results were obtained by a variety of approaches with no clear technique dominating. Both, the detection of drug resistances and the classification of tuberculosis types had similar results than in the previous edition, the former remaining as a very difficult task. In the case of the severity score task, the results support the suitability of assessing the severity based only on the CT image, as the results obtained were very good