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Deep learning has increased the performance of medical decision support systems particularly focusing on images by a good margin. Deep learning usually requires large amounts of data and works best with manual annotations. In the ExaMode project we tackle several challenges related to such AI-based decision support in histopathology. We work on combining training using manual annotations with data that uses only global labels automatically extracted from the reports. Additionally, the project focusses on interpretability of deep learning approaches, as the black box nature of deep learning can be a blocking part for integrating such tools in clinical practice.
Henning Müller studied medical informatics at the University of Heidelberg, Germany, then worked at Daimler-Benz research in Portland, OR, USA. From 1998-2002 he worked on his PhD degree in computer vision at the University of Geneva, Switzerland with a research stay at Monash University, Melbourne, Australia. Since 2002, Henning has been working for the medical informatics service at the University Hospital of Geneva. Since 2007, he has been a full professor at the HES-SO Valais and since 2011 he is responsible for the eHealth unit of the school. Since 2014, he is also professor at the medical faculty of the University of Geneva. In 2015/2016 he was on sabbatical at the Martinos Center, part of Harvard Medical School in Boston, MA, USA to focus on research activities. Henning is coordinator of the ExaMode EU project, was coordinator of the Khresmoi EU project, scientific coordinator of the VISCERAL EU project. Since early 2020 he is also a member of the Swiss National Research Council.
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