Wednesday, July 23, 2025: 10:30 – 11:15

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.

Bio:
Renate Schmidt is University Reader in Computer Science and Leader of Formal Methods Research Group in the Department of Computer Science at the University of Manchester. She served as Chair of the PGR Degrees Panel in the Faculty of Science and Engineering and was Member of the FSE Doctoral Academy Academic Leadership Team and the Faculty Graduate Committee (2021-2024). She is Associate Editor or Editorial Board Member of Artificial Intelligence Journal, Journal of Artificial Intelligence Research, Journal of Automated Reasoning and Journal of Applied Non- Classical Logic. Her research involves the development of both theoretical results and implemented systems for knowledge representation, automated symbolic reasoning and formal methods. Her current research is driven by the aim to develop improved automated support for knowledge representation, ontology extraction, knowledge re- engineering, information hiding/obfuscation, abductive learning and query answering in the context of ontologies.

Wednesday, July 23, 2025: 11:15 – 12:00

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. But how to best achieve these feats, remains a matter of heated academic, political and legal debates. One concept that has taken center stage over the past few years is explainability – or explainable AI (XAI). Put in a nutshell, the idea is that if we can render opaque AI systems explainable with XAI methods that will, in one way or the other, help us ensure their safety, trustworthiness, fairness, and so forth. This reasoning has led to a veritable XAI-hype. In this talk, I take a critical look at contemporary XAI, highlight some of its limitations, and sketch avenues for research addressing these.

Bio:
Lena Kästner is professor for philosophy, computer science and AI at the University of Bayreuth. She has a background in Cognitive Science and Cognitive Neuroscience and received her PhD in philosophy from Ruhr-University Bochum. Prof. Kästner’s research focuses on explanations, intelligence, and causation. Currently, she is also head-PI of the projects “Explainable Intelligent Systems (EIS)” and “For the Greater Good? Deepfakes in Criminal Prosecution (FoGG)”. She is also vice president of the German Society for Philosophy of Science (GWP), vice-director of Bayreuth’s “Research Center for AI in Science and Society (RAIS2), and coordinator of the interdisciplinary Master’s program “Philosophy & Computer Science” in Bayreuth.

Wednesday, July 23, 2025: 13:00 – 13:45

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. This acceleration not only enhances patient comfort and increases scanner throughput but also contributes to a reduction in energy consumption per examination. As such, AI-driven MRI reconstruction represents a concrete example of how technological innovation can simultaneously advance diagnostic performance and promote environmental sustainability in medical imaging. This presentation will place particular emphasis on this application, examining its potential as a key driver of energy efficiency and sustainable radiology practice.

Bio:
PD Dr. med. Judith Herrmann is a board-certified radiologist at the University Hospital in Tübingen, Germany, where she has been working since 2019. She completed her medical studies at the University of Tübingen and began her career in radiology under the mentorship of Prof. Nikolaou. PD Dr. med. Judith Herrmann is a board member of the working group on information technology and a board member of the Young Radiologist Forum within the German Radiological Society. Her research primarily focuses on the application of artificial intelligence in MRI image reconstruction, with a particular interest in its potential to improve efficiency and sustainability in MRI examinations.

Wednesday, July 23, 2025: 13:45 – 14:30

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. Furthermore, there are behaviours in our research community that threaten the validity of published findings. In this talk I will discuss both types of issues with examples from recent papers.

Relevant Papers:

• Copycats: the many lives of a publicly available medical imaging dataset
• Data usage and citation practices in medical imaging conferences
• Augmenting Chest X-ray Datasets with Non-Expert Annotations
• Machine learning for medical imaging: methodological failures and recommendations for the future

Bio:
Prof. Veronika Cheplygina’s research focuses on meta-research in the fields of machine learning and medical image analysis. She received her Ph.D. from Delft University of Technology in 2015. After a postdoc at the Erasmus Medical Center, in 2017 she started as an assistant professor at Eindhoven University of Technology. In 2020, failing to achieve various metrics, she left the tenure track of search of the next step where she can contribute to open and inclusive science. In 2021 she started as an associate professor at IT University of Copenhagen, and was recently appointed as full professor at the same university. Next to research and teaching, Veronika blogs about academic life at https://www.veronikach.com. She also loves cats, which you will often encounter in her work.

Thursday, July 24, 2025: 10:30 – 11:30

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. In my talk I will first talk about how we can obtain visual explanations efficiently and effectively in case of image classification. In the second part I will talk about potential metrics and frameworks for assessing the quality visual explanations. A challenging task due to the difficulty of obtaining ground truth explanations for evaluation.

Bio:
Simone Schaub-Meyer is an assistant professor at the Technical University of Darmstadt, as well as affiliated with the Hessian Center for Artificial Intelligence (hessian.AI). The focus of her research is on developing efficient, robust, and understandable methods and algorithms for image and video analysis. She recently got the renowned Emmy Noether Programme (ENP) grant of the German Research Foundation (DFG) supporting her research on Interpretable Neural Networks for Dense Image and Video Analysis. Before starting her own group, she was a postdoctoral researcher in the Visual Inference Lab of Prof. Stefan Roth. Prior to joining TU Darmstadt, she was a postdoctoral researcher at the Media Technology Lab at ETH Zurich working on augmented reality. She obtained her doctoral degree from ETH Zurich in 2019, awarded with the ETH medal, where she developed novel methods for motion representation and video frame interpolation in collaboration with Disney Research Zurich.