Location: Lecture hall N2045 (Faculty of Applied Computer Science)

Abstract

Computational models and analyses play a crucial role in drug research and development across various stages. These models have become an integral part of the drug development process, addressing a range of challenges. There are two main types of approaches used in these applications: 1) Mechanistic models are utilized to address smaller scale biology and engineering related questions. They help optimize drug design, target engagement, dosing, and PK/PD (pharmacokinetics/pharmacodynamics). 2) Machine learning and statistical models are employed for larger-scale OMICS data analysis. They aid in identifying and characterizing targets, selecting patient populations, and understanding biomarkers of response or resistance. This presentation will showcase recent examples in both areas and explore potential future opportunities for Systems Biology modeling in drug research and development.

Speaker: Dr. Andreas Raue

Biography

Dr. Andreas Raue's research focuses on exploring and developing novel therapeutic approaches in oncology by harnessing advanced technologies such as single-cell sequencing and deep learning. He earned his Ph.D. from Albert-Ludwigs University in Freiburg, Germany, before relocating to the United States. Over the span of more than a decade, he contributed significantly to the fields of biotechnology and pharmaceuticals, conducting discovery and translational research at Merrimack Pharmaceuticals, HiFiBiO Therapeutics, and Novartis. Throughout his tenure, Dr. Raue and his team successfully advanced numerous projects from initial target identification to clinical trials. Until recently, Dr. Raue led the Immuno-Oncology and Hematology Data Science team at Novartis, spearheading efforts to develop and deliver state-of-the-art therapies aimed at improving outcomes for patients with cancer and blood disorders. From May 2024, he will hold the Chair of Modelling and Simulation of Biological Processes at the University of Augsburg.

Location: Large lecture hall (2nd floor, room 047, University Hospital)

Abstract

Clinical decision-making is a complex process that requires the integration of a wide range of patient information and extensive medical knowledge. To effectively support this process, clinical decision support systems should not only be able to form a holistic view of the patient and consider prior knowledge but also be able to convey their reasoning. In light of recent advances in large language models (LLMs), this talk explores the potential of multimodal foundation models in enabling such systems. We first discuss the clinical reasoning process and then present how the integration of knowledge and multimodal patient representations in LLMs could interactively support clinicians in their decision-making. Finally, recent works in radiology report generation and conversational assistance are showcased to illustrate the potential impact of these technologies on radiology and other medical fields.

Speaker: Dipl.-Ing. Matthias Keicher (Research Manager at the Chair of Computer Aided Medical Procedures, Technical University of Munich)

Biography

Matthias Keicher is a senior PhD candidate and group leader at the Chair for Computer Aided Medical Procedures at the Technical University of Munich (TUM) and the interdisciplinary research lab of the hospital Klinikum rechts der Isar in Munich. He leads a research group focused on applying deep learning for vision and language understanding in radiology and neuroradiology, focusing on topics such as automated report generation, structured reporting, and visual question answering. His research interests revolve around using multimodal deep learning to enable holistic clinical decision support systems that can reason over comprehensive patient data. He graduated as a mechanical engineer at TUM specializing in biomedical engineering. Before returning to academia, he gained several years of industrial experience in the healthcare domain, working as a product manager, director of business development, and eventually as CTO and managing director. He also co-founded a health technology startup during this time.

Location: Lecture hall N2045 (Faculty of Applied Computer Science)

Abstract

We will introduce the concept of computational modeling & simulation of organ systems in the human body using the example of cardiac electrophysiology. According to the multi-scale nature of the biological system, we will start from the ion channel level and work our way up to the cellular and eventually organ scale.

We will then use the concept of ensemble simulation spaces to discuss how modeling and simulation can be employed for digital twin applications enabling personalized medicine and for creating large virtual cohorts of digital chimeras enabling in silico trials and machine learning solutions.

Applications include the identification of the best ablation and/or pharmacological strategy to treat atrial fibrillation and prediction of therapy success.

Speaker: PD Dr.-Ing. Axel Loewe (Head of Computational Cardiac Modeling, Karlsruhe Institute of Technology)

Biography

Dr. Axel Loewe heads the Computational Cardiac Modeling Group at Karlsruhe Institute of Technology with a focus on cardiac electrophysiology and biomechanics. His group is committed to method development and the application of computational models to answer questions of clinical relevance at the intersection of engineering, computer science, and medicine. To develop methods and conduct simulation studies, methods of software engineering, algorithmics, numerics, signal processing, data analysis, and machine learning are used. Axel has a track record of fruitful collaboration with leading clinicians to optimize diagnosis and therapy of cardiac diseases.

He studied Electrical Engineering and Information Technology in Karlsruhe and Stockholm and earned a PhD with distinction in Biomedical Engineering in 2013 from KIT. Habilitation "Modeling and Simulation for Medicine" in 2021. Reviewer and guest editor for >40 journals in the fields of Biomedical Engineering, Cardiology and Machine Learning >30 academic distinctions including Patient Safety Award 2018, Gips-Schüle Preis 2018, CinC Young Investigator award 2016.

Location: Small lecture hall (2nd floor, room 048, University Hospital)

Abstract

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 investage 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.

Speaker: Dr. Benjamin Busam (Chair of Computer Aided Medical Procedures, Technical University of Munich)

Biography

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. He 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.

Location: Small lecture hall (2nd floor, room 048, University Hospital)

Abstract

Speaker: PD Dr. Isabelle Miederer (Clinic and Polyclinic for Nuclear Medicine, University Medical Center Mainz)

Biography

Location: Large lecture hall (2nd floor, room 047, University Hospital)

Abstract

Speaker:

Biography

Location: Lecture hall N2045 (Faculty of Applied Computer Science)

Abstract

Speaker: Prof. Dr. Markus Mühlhan (Chair of Neuroscience, Medical School Hamburg)

Biography

Location: Lecture hall N2045 (Faculty of Applied Computer Science)

Abstract

Speaker:

Biography

Location: Small lecture hall (2nd floor, room 048, University Hospital)

Abstract

Speaker:

Biography

Location: Lecture hall N2045 (Faculty of Applied Computer Science)

Abstract

In this talk, I will review some recent developments in the field of interpretable AI models for genomics data.

These models can be used to predict perturbation effects from drug treatment of genetic effects, or infer gene regulatory networks.

Speaker: Prof. Dr. Carl Herrmann (Professor Dept. Bioinformatics, Institute of Pharmacy and Molecular Biotechnology, University Heidelberg)

Biography