In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.

This presentation explores the  potential of artificial intelligence in analyzing multimodal health data, with specific emphasis on electrophysiological signals and medical imaging. The work addresses critical challenges in developing robust, generalizable AI systems for healthcare applications through three interconnected research directions.

The first part examines deep learning approaches for electrocardiogram (ECG) analysis, addressing fundamental limitations in current methodologies. We focus on the critical issues of model generalizability across diverse patient populations and clinical settings, uncertainty quantification in diagnostic predictions, and local calibration techniques to ensure reliable performance in real-world deployment scenarios.

The second section presents advances in magnetic resonance imaging (MRI) reconstruction and super-resolution techniques. We introduce novel approaches leveraging implicit neural representations within unbiased and self-supervised learning frameworks, eliminating the need for extensive paired training data while maintaining high-quality image reconstruction performance.

The presentation concludes with a forward-looking perspective on multimodal fusion, exploring how joint learning of ECG signals and cardiac medical images can be integrated to develop comprehensive digital twins of the human heart. This integrated approach promises to revolutionize personalized cardiac care by providing unprecedented insights into individual cardiac function and pathology.