Artificial Intelligence in Medicine (IN2403)

At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.

  • Introduction: Clinical motivation, clinical data, clinical workflows
  • ML for medical imaging• Data curation for medical applications
  • Domain shift in medical applications: Adversarial learning and Transfer learning
  • Self-supervised learning and unsupervised learning
  • Learning from sparse and noisy data
  • ML for unstructured and multi-modal clinical data
  • NLP for clinical data• Bayesian approaches to deep learning and uncertainty
  • Interpretability and explainability
  • Federated learning, privacy-preserving ML and ethics
  • ML for time-to-event modeling, survival models
  • ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics
Julia A. Schnabel
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.