Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification
Serge Kozlovski, Vitali Liauchuk, Yashin Dicente Cid, Vassili Kovalev, Henning Müller
Abstract
ImageCLEF is a part of the Conference and Labs of the Evaluation Forum (CLEF) initiative and includes a variety of tasks dedicated to multimodal image information retrieval, including image classification and annotation. The tuberculosis (TB) task is one of the ImageCLEF tasks which started in 2017 and changed from year to year. The 2021 edition was dedicated to the automatic classification of five TB types: Infiltrative, Focal, Tuberculoma, Miliary, Fibro-cavernous. The task itself repeated one of the original subtasks from 2017 but the dataset was significantly changed. In 2021, 11 groups from 9 countries participated in the task and submitted at least one successful run. The task results can be compared to the TB type classification task results in the 2017 and 2018 editions. Although top scores were not improved compared to the previous editions, the participants’ results allow us to analyze the effectiveness of applying recent deep learning approaches to the task.