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When? December 4, 2025
When? December 4, 2025
Where? Room A108, Polo Ferrari 1, University of Trento
Where? Room A108, Polo Ferrari 1, University of Trento
Full program will be available soon!
Full program will be available soon!
Participation to the workshop is free of charge but registration is required!
Participation to the workshop is free of charge but registration is required!
Bio
Bio
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 was coordinator of the ExaMode EU project, the Khresmoi EU project and scientific coordinator of the VISCERAL EU project. Since early 2020, he is also a member of the Swiss National Research Council,where he leads the program committee on thematic and solution-oriented research.
Keynote Speaker
Keynote Speaker
Prof. Henning Müller
Prof. Henning Müller
Full Professor in Computer Science and in Radiology
HES-SO Valais-Wallis and University of Geneva
Keynote
Keynote
Multimodal Medical Machine Learning
Multimodal Medical Machine Learning
Bio
Bio
Katja Bühler is the Scientific Director of VRVis GmbH, a non-profit research centre for Visual Computing in Vienna, Austria. Founded in 2000, VRVis's aim is to promote technology transfer between science and industry. Katja's academic background is in Mathematics (Dipl.math., KIT, Germany) and Computer Science (Dr.techn., TU Vienna, Austria). She joined VRVis as a Senior Researcher in 2002, was promoted to Group Leader of Medical Visualisation in 2003 and became Division Coordinator of Complex Systems in 2010. In recognition of her efforts to realise innovative Visual Computing solutions in close cooperation with industry and science, she received the Austrian "science2business Award" in 2012 and the TU Vienna Women's Award in 2020. As head of the Biomedical Image Informatics Group at VRVis, Katja's scientific research focuses on methods that enable intuitive and efficient access to the information encoded in imaging and related (spatial) data. Together with her interdisciplinary team, she is realising this vision by combining expertise in image analysis, AI, data mining, visualisation, and HCI to develop intelligent, human-centred solutions with a strong application focus on medicine and life sciences. In addition to her work at VRVis, she is a member of the board of Austrian Bioimaging and, most recently, also a member of the board of the Association for the Promotion of Digital Humanism in Vienna.
Keynote Speaker
Keynote Speaker
Prof. Katja Bühler
Prof. Katja Bühler
Scientific Director
Vienna Research Center for Visual Computing
Keynote
Keynote
Uncertainty, Reliability, and Trust in Image-Based Decision-Making
Uncertainty, Reliability, and Trust in Image-Based Decision-Making
The effective integration of AI-supported decision-making in clinical practice depends on understanding and managing uncertainty, a multifaceted challenge that encompasses reliability, transparency, and trust.
The talk will outline a conceptual and operational framework connecting these elements, illustrating how different forms of uncertainty influence both model performance and clinical adoption. Drawing on recent research, the Keynote will explore strategies to strengthen reliability and interpretability in image-based workflows: from quantifying and propagating uncertainty throughout the decision pipeline to designing explainable mechanisms that render confidence levels explicit and actionable. Approaches for uncertainty-aware learning and validation will be discussed, alongside methods to calibrate trust through transparent interfaces and continuous human–machine feedback loops. Finally, the Keynote will consider how embedding reliability and trustworthiness by design can enable sustainable, responsible, and clinically meaningful AI integration.
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