Period (from – till): 9 November 2020 - 1 February 2021
Course coordinator: Dr. Alexander Leemans
Faculty
Dr. Alexander Leemans, UMC Utrecht/Imaging & Oncology Division, lecturer
Dr. Hugo Kuijf, UMC Utrecht/Imaging Imaging & Oncology, lecturer
Renée Allebrandi, MA (course registration)
Course description:
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 diffusion 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 students will learn how to work with real diffusion MRI data.
Literature/study material used:
Deep Learning, by Goodfellow, Bengio, Courville,
https://www.deeplearningbook.org/
hand-outs provided by lecturers
suggested reading material
Details:
https://mix.isi.uu.nl/courses/capita-selecta-medical-imaging-uu/
Registration
You can register for this course via
Osiris Student. More information about the registration procedure can be found
here on the Studyguide.
Students from outside the UU or TU/E partnership can register for this course by sending an email to
mix@isi.uu.nl. Please include your name, student number, Master’s programme and the course code.
Mandatory
No.
Optional for students in other GSLS Master’s programme:
Yes.