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Course module: BMB502217
BMB502217
Capita Selecta in Medical Image Analysis TU/Eindhoven
Course info
Course codeBMB502217
ECTS Credits5
Category / LevelM (Master)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Medicine; Graduate School of Life Sciences; Medical Imaging;
Contact personR.M. Allebrandi
Telephone+31 88 7569652
E-mailR.Allebrandi@umcutrecht.nl
Lecturers
Contactperson for the course
R.M. Allebrandi
Other courses by this lecturer
Teaching period
MASTER  (20/08/2018 to 17/08/2019)
Teaching period in which the course begins
MASTER
Time slot-: Not in use
Study mode
Full-time
Remark4 Feb - 19 April 2019. This course is given at the TU Eindhoven. Please register there with course code 8DM20!
Course application processadministratie onderwijsinstituut
Enrolling through OSIRISNo
Enrolment open to students taking subsidiary coursesNo
Pre-enrolmentNo
Waiting listNo
Course placement processadministratie onderwijsinstituut
Course goals
Learning objectives
  1. Comprehension of the complexity and data structure of diffusion MRI. Knowledge and application of simple algorithms to extract information from the data.
  2. Knowledge of medical visualization methods and their main components.
  3. Application of medical visualization methods to practical problems.
  4. Knowledge of methods to validate medical image analysis algorithms.
  5. Knowledge of the concepts of advanced image registration methods and comprehension of their application to clinical problems
Content
Period (from – till): 4 February - 19 April 2019
 
Course coordinator: Renée Allebrandi, MA (course registration)
 
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.
Entry requirements
Required materials
-
Instructional formats
Lecture

Tests
Final result
Test weight100
Minimum grade5.5

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Kies de Nederlandse taal