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ImageCLEF2019: Tuberculosis - Severity Scoring and CT Report with Neural Networks, Transfer Learning and Ensembling

The diagnosis of tuberculosis is challenging. We present our approach for classifying whether a patient has high or low severity tuberculosis and for detecting which lung is involved, if there is decreased capacity, and if there are pleurisies, calcifications or cavities present. Our best results for the CT report task were obtained by converting volume images into an 8x4 montage of sagittal or coronal images and ensembling the results of separate networks trained separately on sagittal and coronal montage images. The best results for the severity scoring were obtained by ensembling the results from the CT report with the provided metadata.