Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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Session 4a: Imaging
Session Topics: Agentic AI, Foundation Models, Generative Models, Graph Neural Networks, Physics-informed Machine Learning, Reinforcement Learning, Probabilistic Methods, Uncertainty Quantification, Audio, Other, Graphs, Image, Multimodal Data, Simulation Data, Tabular Data, Text, Time Series, Video, Other, Core Machine Learning, Aeronautics, Space & Transport, Energy, Earth & Environment, Health, Information, Matter
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Choose from expert-led talks running simultaneously to explore AI topics that match your interests. | ||
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9:00am - 9:12am
ID: 221 / a Thu | LAB 9h Parallel S 4a: 001 Modalities: Image, Time Series, Video Methods: Other Application Domain: Health AI-enabled Colorimetric Multi-Biomarker Sensing Patch for Neonatal Monitoring 1Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.; 2School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.; 3Silklab, Dept. of Biomedical Engineering, Tufts University, Medford, MA, USA.; 4Comprehensive Pneumology Center with the CPC-M bioArchive and Institute of Lung Health and Immunity, Helmholtz Center Munich, Member of the German Center of Lung Research (DZL), Munich, Germany; 5Dr. von Hauner Children’s Hospital, Hospital of the Ludwig-Maximilians-Universität München, Munich, Germany; 6School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK.; 7School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany; 8Dept. of. Electrical and Computer Engineering, Tufts University, Medford, MA, USA.; 9Dept. Of Physics, Tufts University, Medford, MA, USA. 9:12am - 9:24am
ID: 200 / a Thu | LAB 9h Parallel S 4a: 002 Modalities: Image Methods: Generative Models, Physics-informed Machine Learning Application Domain: Health Implicit Neural Representation (INR) meets Multi-Contrast MRI Reconstruction 1School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; 2GE HealthCare, Munich; 3Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich; 4Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; 5Technische Hochschule Ingolstadt, Ingolstadt, Germany; 6School of Natural Sciences, Technical University of Munich, Munich, Germany; 7King’s College London, London, United Kingdom 9:24am - 9:36am
ID: 239 / a Thu | LAB 9h Parallel S 4a: 003 Modalities: Image Methods: Physics-informed Machine Learning Application Domain: Health Deep Learning Reconstruction of Diffusion Spectrum Imaging from Undersampled q-Space Measurements 1Technische Hochschule Ingolstadt, Germany; 2Technical University of Munich, Germany 9:36am - 9:48am
ID: 374 / a Thu | LAB 9h Parallel S 4a: 004 Modalities: Graphs, Image Methods: Foundation Models, Graph Neural Networks Application Domain: Health HematoGraph: Graph-Aware Hierarchical Pooling for Cell-Level Hematology Classification 1Helmholtz Munich, Germany; 2TUM; 3LMU 9:48am - 10:00am
ID: 291 / a Thu | LAB 9h Parallel S 4a: 005 Modalities: Image Methods: Foundation Models Application Domain: Information Contour Proposal Networks with Deep Refinement for Dense High-Throughput Instance Segmentation 1C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Germany; 2Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany; 3Helmholtz AI, Research Center Jülich, Germany; 4Institute for Computational Visualistics, University of Koblenz, Germany | ||