SZTAKI @ ImageCLEFmed 2020 Tuberculosis Task

    In this paper we describe our submission to theImageCLEFmed 2020 Tuberculosis task and discuss additional results on the training set with various neural networks. After some centralization and normalization we independently categorized the 2D slices with convolutional neural networks (traditional and residual feed-forward networks) and we aggregated the individual predictions based on the positions of the lung and the slices. Our additional experiments with various aggregation methods indicate that individual slices do not necessary contain enough information about such complex structures.

    Bence Lestyan, András Benczúr, Bálint Daróczy
    In working notes of CLEF 2020, ImageCLEF, Thessaloniki, Greece