Artificial Intelligence in Medicine II (IN2408)
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Content
Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis & population stratification, geometric deep learning: point clouds & meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis
- Learning Outcome
At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.
- Preconditions
IN2403 Artificial Intelligence in Medicine
Julia A. Schnabel
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.