Key information
- Faculty
- Faculty of Engineering Sciences
- Teaching department
- Medical Physics and Biomedical Engineering
- Credit value
- 15
- Restrictions
-
Basic knowledge and experience in Python are necessary for the second part of the lab-sessions and the coursework. However, students with prior experience in Matlab, R or other programming languages may be acceptable given an introductory Python tutorial outside of the module.
Please note this module is restricted by the size of the UCL computer labs.
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
Description
This module provides an essential introduction to theory and practice for information processing methods in medical imaging and computing, including the latest developments in machine learning and deep learning medical image processing. The focus of the module is on the registration and segmentation of medical images, alongside an overview of how biomarkers derived from image processing can be used to test scientific hypotheses or applied in clinical contexts. The module also includes a primer on deep learning, which provides a foundation for understanding deep learning approaches to image registration and segmentation. This module assumes a good prior knowledge of basic linear algebra (e.g., matrix arithmetic), calculus, and probability theory, and competent programming skills in Python or Matlab.
Module format
The module comprises conventional lectures, which incorporate interactive discussion elements, and computer laboratory sessions, where the practical elements outlined during the lectures are put into practice, running code to register, segment and statistically analyse medical images.
Module deliveries for 2024/25 academic year
Intended teaching term:
Term 2 ÌýÌýÌý
Undergraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
100%
Coursework
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
9
- Module leader
-
Dr Jamie Mcclelland
- Who to contact for more information
- medphys.teaching@ucl.ac.uk
Intended teaching term:
Term 2 ÌýÌýÌý
Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
100%
Coursework
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
24
- Module leader
-
Dr Jamie Mcclelland
- Who to contact for more information
- medphys.teaching@ucl.ac.uk
Last updated
This module description was last updated on 19th August 2024.
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