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| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2025W |
| Learning Outcomes |
Competences |
| Students have an understanding of deep learning techniques applied to medical imaging. The course covers topics such as image generation for X-ray, CT, MRI, and ultrasound, and modern reconstruction methods. Students explore image segmentation, registration, solving inverse problems, and handling noisy data. They also develop skills to improve model robustness and understand federated learning for data privacy. Practical exercises showcase how deep learning impacts medical imaging in clinical practice.
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| Skills |
Knowledge |
| Students learn to apply deep learning methods for image reconstruction, segmentation, and registration across medical imaging modalities. They implement modern reconstruction techniques, solve inverse problems, and improve image quality. The course covers both classical and deep learning-based approaches. Students develop strategies to train models on noisy and sparse data while enhancing robustness against adversarial attacks and domain shifts. They also explore federated learning for secure, distributed model training.
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By the end of the course, students understand medical imaging modalities and how deep learning can be integrated. They know about inverse problems and the evolution of reconstruction techniques. Additionally, students have gained insight into handling noisy data, improving robustness, and addressing domain adaptation. They also have explored federated learning and its role in secure model training.
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| Criteria for evaluation |
Exam at the end of the semester
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| Methods |
group work on projects, slide presentations, discussion, report
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| Language |
Englisch |
| Changing subject? |
No |
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