Learning objectives
- Comprehension of the complexity and data structure of diffusion MRI. Knowledge and application of simple algorithms to extract information from the data.
- Knowledge of medical visualization methods and their main components.
- Application of medical visualization methods to practical problems.
- Knowledge of methods to validate medical image analysis algorithms.
- Knowledge of the concepts of advanced image registration methods and comprehension of their application to clinical problems
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Period (from – till): 3 February - 17 April 2020
Course coordinator: Renée Allebrandi, MA (course contact person)
Course aims and content:
PLEASE NOTE THAT THIS COURSE IS TAUGHT IN EINDHOVEN
This course covers a number of state-of-the-art techniques and topics in medical image analysis. It is a specialisation course for those with a general understanding of medical image analysis looking to deepen their knowledge. The topics of this year are
- Deep Learning
- Image Registration and Validation
You will learn about machine learning, (convolutional) neural networks and how to train them. The second part of the course considers nonlinear image registration and proper methods to set up a validation study. Each part of the course will come with a large group assignment on actual medical data to give you hands-on experience, both in a deep learning approach to a medical image analysis problem and in nonlinear image registration for clinical data.
Literature/study material used
Slide hand-outs, Deep Learning by Goodfellow, Bengio and Courville; other material will be made available
Registration
Please register at TU/e, course code 8DM20, at least 4 weeks before start of the course. Osiris registration will be done retroactively when results from the TU/e are received.
Mandatory
No.
Optional for students in other GSLS Master’s programme:
No.
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