Deep Learning for Inverse Problems in Medical Imaging (IN2107, IN45096)
In medical imaging, the reconstruction of high-quality images from incomplete or corrupted data often involves solving inverse problems. Deep learning has emerged as a powerful tool for addressing these challenges, offering approaches to improve image reconstruction quality, enhance computational efficiency, and tackle complex non-linearities.
This seminar explores the concepts of deep learning for inverse problems, focusing on their applications in medical imaging. Selected materials from recent methodological advances will be covered as well as key challenges and opportunities in leveraging deep learning for clinical applications.
Key topics to be covered include: • Introduction to inverse problems in medical imaging • Deep learning approaches for solving inverse problems • Applications in various medical imaging modalities (e.g., MRI, CT, PET) • Comparison of traditional and deep learning-based methods • Emerging trends and clinical implications
Requirements:
- Background in image processing and machine learning/deep learning
- Interest in medical imaging
- Interest in research
On-site attendance is mandatory for students.
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.