Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Our world is 3D and so is the patient. But visual camera observations are 2D. Advancements in digital imaging now enable capturing rich 3D information of our surrounding, transforming our ability to digitally perceive, present, and analyze data. By combining multiple data sources or frames in a video, we can create context-enhanced digital copies of the physical world as well as the patient - and apply data-driven interpretations and processing. Data sources reach from ceiling mounted cameras in the OR to endoscopic images or robot-mounted sensors. In this talk, we want to look into underlying core ideas of example 3D computer vision pipelines and investigate how these approaches can be used in clinical applications. As a basis for a discussion at this exciting interdisciplinary crossroad, we dive into the subtopics 3D digital reconstruction, data curation for XR, and robot-assisted surgery.

Referent: Dr. Benjamin Busam

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Benjamin Busam is a Senior Research Scientist with the Technical University of Munich. He coordinates the Computer Vision activities at the Chair for Computer Aided Medical Procedures, I16. Formerly Head of Research at FRAMOS Imaging Systems, he led the 3D Computer Vision & AI Team at Huawei Research, London from 2018 to 2020. Benjamin studied Mathematics at TUM (Germany), ParisTech (France) and at the University of Melbourne (Australia), before he graduated with distinction at TU Munich in 2014. In continuation to a mathematical focus on projective geometry and 3D point cloud matching, he now works on 2D/3D computer vision and sensor fusion and applications into the medical domain. For his work on adaptable high-resolution real-time stereo tracking, he received the EMVA Young Professional Award 2015 from the European Machine Vision Association and was awarded Innovation Pioneer of the Year 2019 by Noah's Ark Laboratory, London. He was given multiple Outstanding Reviewer Awards at 3DV 2020, 3DV 2021, and ECCV 2022.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Due to the rapid progress in developing experimental techniques, establishing and improving analysis methods is one of the major challenges in computational life sciences. For many analysis tasks, however, the limitations and performance of competing methods remain unknown, and there are no clear rules or guidelines for selecting the optimal analysis method. Benchmark studies have proven to be valuable tools for evaluating the performance and applicability of analysis approaches. However, they are often subject to methodological limitations and deficiencies, leading to potential bias in the results.

In my presentation, I will give an overview of novel approaches developed in my group in the context of mathematical modeling and omics analyses. In particular, I will summarize our ongoing efforts to improve the methodology of benchmark studies. By generally incorporating rigorous planning, design, and analysis principles in benchmark studies, we aim to promote the development of novel analysis approaches and the identification of decision rules for optimal method selection in practice. Especially in view of the enormous efforts to apply deep learning methods in all areas of research, reliable performance comparisons are of great importance.

Referent: Dr. Clemens Kreutz

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Dr. Clemens Kreutz is the Head of the Methods of Systems Biomedicine (MSB) group and Deputy Director at the Institute of Medical Biometry and Statistics, Medical Center, Faculty of Medicine, University of Freiburg. Since 2018, he has held a permanent position as Group Leader at the institute, and in 2021, he was appointed Deputy Director. Dr. Kreutz's research focuses on the mathematical modeling and analysis of high-throughput data. His work within the field of systems biology has significantly advanced statistical methods for parameter estimation, model selection, and experimental design, contributing to the understanding and modeling of complex biological processes.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referent:  Dr. Robert Peach

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Bioimpedance analysis is an established non-invasive method that is already in clinical use for body composition measurement or electrical impedance tomography. However, novel instrumentation and signal processing approaches enable significant improvements of these applications as well as new measurement approaches. This presentation will give a brief insight into the theory of bioimpedance analysis and discuss instrumentation approaches. Subsequently, examples of new scientific measurement approaches will be presented. The focus will be on the detection of skeletal muscle contractions (impedance myography) and pulse wave detection (impedance plethysmography).

Referent: Prof. Dr. Roman Kusche

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Roman Kusche has been a Professor in the Department of Computer Science at HAW Hamburg since 2023, where he leads the Medical Sensors Lab. His research interests include the development of novel biomedical measurement methods and related medical electronic devices. Before joining HAW Hamburg, he was the Head of the Department for Individualized Therapy and led the Medical Electronics research group at Fraunhofer IMTE. Roman Kusche has a background in electrical engineering and received his Ph.D. from the Institute of Medical Engineering at the University of Lübeck.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Artificial Intelligence (AI) is transforming our world and society with extraordinary speed. What does this mean for biomedical research and disease detection? What opportunities and risks arise from this? In my talk, I will present examples from the biomedical and clinical research conducted by my research group at the Helmholtz Center Munich. I will cover the fundamentals of machine learning and AI and showcase applications that allow individual cells to be characterized with unprecedented accuracy. These technological breakthroughs promise new perspectives for both basic research and the personalized medicine of the future.

Referent:  Dr. Carsten Marr

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Carsten Marr is the founding director of the Institute of AI for Health at Helmholtz Munich, a European center for applied artificial intelligence. His goal is to develop AI-based methods to improve the diagnosis, treatment, and understanding of diseases. After studying theoretical physics at the Technical University of Munich and completing his diploma thesis at the Max Planck Institute of Quantum Optics, Carsten switched from physics to theoretical biology. His PhD thesis at Technical University of Darmstadt focused on the architecture of biological networks and was awarded the best of its year in the Department of Biology. After postdoctoral research stays in Munich, Bremen and Edinburgh, he started his research group at the Helmholtz Munich in 2013 and became deputy head of the Institute of Computational Biology. In interdisciplinary projects with experimentalists, biomedical experts, and clinicians, he pioneered the training of deep neural networks on life science data for the prediction of stem cell decisions from microscopic images and the identification of leukemia from blood and bone marrow smears. He has received several awards for his research and analysis of single cell data as well as an ERC Consolidator Grant for the training of AI models for the automated analysis of blood diseases. 

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referent: Prof. Ralf Seepold

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referentin:  Anna Danese, M.Sc.

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Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Wearables have been the dominant consumer monitoring device for the past decades, however, not the ideal one. Battery dependency, correct placement, good contact with skin, etc., all impose requirements and restrictions that can’t be met by everyone (e.g., vulnerable populations, skin conditions, etc.). In light of this and in accordance with the ultimate idea of ubiquitous computing, the interaction between the user and sensor should be minimized or ideally removed entirely. This is being achieved with improvements and availability of sensors – namely RGB cameras, thermal cameras, radars, LIDARs, and others – accompanied by immense development of machine learning, specifically deep neural networks. In this talk we will overview existing methods for unobtrusive contact-free monitoring of physiological parameters and some psychological states that can be derived from them.

Referent: Dr. Gašper Slapničar

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Dr. Gašper Slapničar is the head of the ambient intelligence group at the department of intelligent systems, Jožef Stefan Institute. He finished his undergrad studies in computer science from the faculty of computer and information science, university of Ljubljana, and then pursued his PhD at Jožef Stefan international postgraduate school, focusing on contact-free physiological monitoring using radar and optical sensors. He is the author of over 30 scientific papers, many published in impact factor journals and at established conferences such as ICCV and BHI. He is also a regular program committee member for the computer vision for physiological measurement (CVPM) workshop. His ongoing research includes unobtrusive contact-free monitoring of complex states, including emotions and well-being.

Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)

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Referent:  Dr. Johannes Tran-Gia

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