In this paper we describe our submission to the ImageCLEFmed 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.