Transfer Learning and Domain Adaptation in Medical Imaging (IN0014, IN2107)
Transfer learning enables the effective utilization of knowledge gained from one task or domain to enhance performance in another, while domain adaptation focuses on adapting models trained on a particular domain to perform well in related but different domains. This seminar looks at the concepts of transfer learning and domain adaptation in general and with the application in medical imaging. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed.
Key topics to be covered include:
- Introduction to transfer learning and domain adaptation
- Implications in the context of medical imaging
- Examples of transfer learning and domain adaptation in medical imaging
- State-of-the-art methods
- Clinical applications
Requirements:
- Background in image processing and machine learning/deep learning
- Interest in medical image analysis
- Interest in research
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.