Deep Medical Anomaly Segmentation

Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.

This includes, but is not limited to:

  • Reconstruction-based anomaly segmentation
  • Probabilistic models, i.e., anomaly likelihood estimation
  • Generative models
  • Self-supervised-, contrastive methods
  • Unsupervised methods
  • Clinical Applications
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.