Unsupervised Anomaly Detection in Medical Imaging
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
Please register via the TUM matching system: https://matching.in.tum.de
Check the intro slides here:
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.