@InProceedings{Hicks2018a, title = {Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis}, author = {Steven Alexander Hicks and Konstantin Pogorelov and Thomas de Lange and Mathias Lux and Mattis Jeppsson and Kristin Ranheim Randel and Sigrun L. Eskeland 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 = {490--493}, publisher = {ACM Press}, abstract = {In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.}, doi = {10.1145/3204949.3208113}, url = {https://dl.acm.org/citation.cfm?id=3208113} }