Coordinator
Dr A.L.G. Leemans (Alexander)
Contact
Dr A.L.G. Leemans (Alexander) (A.Leemans@umcutrecht.nl)
Faculty
Dr Alexander Leemans
Dr Hugo Kuijf
Date
mid November 2020 – February 2021; for the schedule please check the website of the Master’s programme Medical Imaging
Register by sending an e-mail to ImagO. Please note: this course will be taught partly online in Teams and partly onsite in the Hijmans van den Bergh building or UMCU. To access the Teams account you need a solis id. If you do not yet have one, apply for one on time.
Location
University Medical Center Utrecht
Course content
This course consists of two independent topics. During the first half of the course topics on deep learning for medical image analysis will be introduced:
- Machine Learning fundamentals
- Deep Learning
- Convolutional Neural Network
- Network Architectures
- Medical Image Analysis applications
During practical sessions students will improve their understanding of the above topics. Additionally there will be a homework group assignment to be handed in at the end of the course.
The second half of the course covers theory and practice of processing, analysing and visualising difffusion MRI data. Key concepts and practical considerations of data processing and analysis are explained. Topics include
- Quality assessment
- Artifact correction
- Diffusion approaches
- Fiber tractography
- Automated analyses
- Visualisation methods
During computer practical sessions participants will learn how to work with real diffusion MRI data.
Upon completion of the course the student :
- will be familiar with the concepts of machine learning and deep learning
- will be familiar with the latest developments and clinical applications of these techniques
- has a basic understanding of neural networks for medical image analysis
- can identify common MRI artifacts present in diffusion MRI data
- will know the basic processing steps required for diffusion MRI
- is able to discriminate between different diffusion MRI model strategies
- will have basic hands-on knowledge of analysing and visualizing diffusion MRI data
- will understand the limitations and pitfalls in the context of neuroscientific and biomedical applications
Credits
You will be awarded 3 EC for attendance; a further 2 EC can be obtained by passing the final exam.
Literature
Deep Learning, by Goodfellow, Bengio, Courville, https://www.deeplearningbook.org/
hand-outs provided by lecturers
suggested reading material