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UID:news68@rds.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20221202T183228
DTSTART;TZID=Europe/Zurich:20221216T123000
SUMMARY:From clinical decision support and explainable AI towards digital t
 wins
DESCRIPTION:Abstract:\\r\\nThis presentation will summarize the main work d
 one in the research group of Henning Müller at the HES-SO Valais in Sierr
 e on medical multimodal data analysis. This work ranges from image creatio
 n differences\, to biomarker extraction from radiology and histopathology 
 images towards clinical decision support. An essential part of integrating
  the mainly deep learning based approaches is to be able to explain decisi
 ons and predictions\, whereas deep learning networks are usually black box
 es.\\r\\nAll these aspects can help to create a digital view on patients w
 here several options for prevention or treatment can be adapted interactiv
 ely. This is also name digital twins in industry and is a concept that we 
 expect to become more common in the medical field when longitudinal data o
 n patients is available.\\r\\nBiography:\\r\\nHenning Müller studied medi
 cal 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\, Switzerla
 nd with a research stay at Monash University\, Melbourne\, Australia. Sinc
 e 2002\, Henning has been working for the medical informatics service at t
 he University Hospital of Geneva. Since 2007\, he has been a full professo
 r at the HES-SO Valais and since 2011 he is responsible for the eHealth un
 it 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 Mar
 tinos Center\, part of Harvard Medical School in Boston\, MA\, USA to focu
 s 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.
X-ALT-DESC:<p><u>Abstract:</u></p>\n<p>This presentation will summarize the
  main work done in the research group of Henning Müller at the HES-SO Val
 ais in Sierre on medical multimodal data analysis. This work ranges from i
 mage creation differences\, to biomarker extraction from radiology and his
 topathology images towards clinical decision support. An essential part of
  integrating the mainly deep learning based approaches is to be able to ex
 plain decisions and predictions\, whereas deep learning networks are usual
 ly black boxes.</p>\n<p>All these aspects can help to create a digital vie
 w on patients where several options for prevention or treatment can be ada
 pted interactively. This is also name digital twins in industry and is a c
 oncept that we expect to become more common in the medical field when long
 itudinal data on patients is available.</p>\n<p><u>Biography:</u></p>\n<p>
 Henning Müller studied medical informatics at the University of Heidelber
 g\, 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 Univ
 ersity of Geneva\, Switzerland with a research stay at Monash University\,
  Melbourne\, Australia. Since 2002\, Henning has been working for the medi
 cal 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 re
 sponsible for the eHealth unit of the school. Since 2014\, he is also prof
 essor 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 coordinat
 or of the ExaMode EU project\, was coordinator of the Khresmoi EU project\
 , scientific coordinator of the VISCERAL EU project. Since early 2020 he i
 s also a member of the Swiss National Research Council.</p>
DTEND;TZID=Europe/Zurich:20221216T133000
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