Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis
| Autor: | Sigrun Losada Eskeland, Steven Alexander Hicks, Konstantin Pogorelov, Thomas de Lange, Kristin Ranheim Randel, Mathias Lux, Michael Riegler, Mattis Jeppsson, Pål Halvorsen |
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| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Disease detection business.industry Computer science Deep learning education Significant part 02 engineering and technology Data science Medical documents 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 030211 gastroenterology & hepatology 020201 artificial intelligence & image processing Report generation Medical journal Artificial intelligence business |
| Zdroj: | MMSys |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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