ImageCLEF 2019: Deep Learning for Tuberculosis CT Image Analysis
In this article, we present the methodologies used in our participation in the two subtasks of the ImageCLEF 2019 Tuberculosis Task (SVR and CTR) Our contributions are essentially based on deep learning and other machine learning techniques. In addition to the use of deep learners, semantic descriptors are tested to represent patients CT scans. These features are extracted after a first learning step. Our submissions on the test corpus reached AUC value of about 65% in the SVR task and an average AUC value of about 63% in CTR. These results offered us the seventh and the eighth places in SVR and CTR, respectively. We believe that our contributions could be further improved and might give better results if they applied properly and in an optimized way.
CLEF