Invited Talks

Advances in Automated MRI Processing for Population-Scale Musculoskeletal Research

Hendrik Möller & Robert Graf, TU Munich Abstract We present recent advances in automatic image processing pipelines designed for large-scale cohort studies, focusing on applications in the NAKO (German National Cohort) and back pain research. We first highlight our work on Image2Image translation methods—including denoising diffusion models and Pix2Pix networks—that enable missing sequences, correct reconstruction errors in water-fat imaging (MAGO-SP), and perform MRI-to-CT translation for accurate bone segmentation. The second part will focus on segmentation, showcasing models such as SPINEPS and TotalVibeSegmentator and our approach to generating new ground truths for training.

Artificial Intelligence in Radiology: A game-changer for sustainable medicine or just a hype?

PD Dr. med. Judith Herrmann, University Hospital Tübingen Abstract Artificial intelligence (AI) is increasingly being integrated into radiological workflows, offering significant potential for improving both efficiency and image quality. Its applications are diverse, ranging from automated image acquisition and interpretation to workflow optimization and predictive analytics. A particularly promising area lies in AI-based reconstruction for magnetic resonance imaging (MRI). Deep learning (DL) algorithms enable the reconstruction of high-quality images from highly undersampled raw data, thereby substantially reducing scan times.

Computing and evaluating visual explanations

Prof. Dr. Sc. Simone Schaub-Meyer, Visual Inference Lab, TU Darmstadt Abstract Recent developments in deep learning have led to significant advances in many areas of computer vision. However, especially in safety critical scenarios, we are not only interested in task specific performance but there is a critical need to be able to explain the decision process of a deep neural networks despite its complexity. Visual explanations can help to demystify the inner workings of these models, providing insights into their decision-making processes.

Curious findings about medical image datasets

Prof. Veronika Cheplygina PhD, IT University of Copenhagen Abstract It may seem intuitive that we need high quality datasets to ensure for robust algorithms for medical image classification. With the introduction of openly available, larger datasets, it might seem that the problem has been solved. However, this is far from being the case, as it turns out that even these datasets suffer from issues like label noise and shortcuts or confounders.

Just the Right Amount: SNOMED CT Content Extraction

Dr.-Ing. Renate Schmidt, University of Manchester Abstract SNOMED CT is established technology of AI in health, where it provides the basis for medical terminological services used to support consistent data capture, easy data sharing and convenient analysis of data. SNOMED CT is a large knowledge base (ontology) of definitions of medical codes used by clinicians in health care sectors worth-wide. After a brief introduction of medical ontologies and their benefits, this talk will review subontologies, a bespoke technique for procuding concise extracts of SNOMED CT, their key features, use cases, successful results and their development in a successful collaboration with industry.

Responsible AI -- What does it take?

Prof. Dr. Lena Kästner, University Bayreuth Abstract As AI technology becomes increasingly used in the public sphere, including in such vulnerable settings as courts and hospitals, questions about the societal demands of deploying AI are becoming ever more relevant. General calls to make the use of AI “responsible”, viz. that the systems in question should be safe, trustworthy, fair, privacy respecting, etc. are echoed by researchers, legal institutions, NGOs and customer protection services alike.