@InProceedings{Hicks2018, title = {Mimir: an automatic reporting and reasoning system for deep learning based analysis in the medical domain}, author = {Steven Alexander Hicks and Sigrun L. Eskeland and Mathias Lux and Thomas de Lange and Kristin Ranheim Randel and Mattis Jeppsson and Konstantin Pogorelov and Pal Halvorsen and Michael Riegler}, booktitle = {MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference}, year = {2018}, address = {New York (NY)}, month = {Juni}, pages = {369--374}, publisher = {ACM Press}, abstract = {Automatic detection of diseases is a growing field of interest, and machine learning in form of deep learning neural networks are frequently explored as a potential tool for the medical video analysis. To both improve the "black box"-understanding and assist in the administrative duties of writing an examination report, we release an automated multimedia reporting software dissecting the neural network to learn the intermediate analysis steps, i.e., we are adding a new level of understanding and explainability by looking into the deep learning algorithms decision processes. The presented open-source software can be used for easy retrieval and reuse of data for automatic report generation, comparisons, teaching and research. As an example, we use live colonoscopy as a use case which is the gold standard examination of the large bowel, commonly performed for clinical and screening purposes. The added information has potentially a large value, and reuse of the data for the automatic reporting may potentially save the doctors large amounts of time.}, doi = {10.1145/3204949.3208129}, url = {https://dl.acm.org/citation.cfm?id=3208129} }