Course unit details:
Mathematical Computing for Medical Imaging
| Unit code | IIDS67462 |
|---|---|
| Credit rating | 15 |
| Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
| Teaching period(s) | Semester 2 |
| Available as a free choice unit? | No |
Overview
This unit will introduce mathematical and computing techniques used in medical image analysis.
It will cover optimsation techniques, statistical shape modelling, classification/regression and an introduction to neural networks. Examples from a range of medical image analysis problems will be included.
Students will learn how the different algorithms work, but more importantly, when to use each approach. The course includes both the mathematical techniques and an introduction to Python libraries which implement them. Students will learn Python programming skills in order to write software to solve practical problems.
Pre/co-requisites
Students are assumed to have a basic knowledge of linear algebra (matrices and vectors) and basic calculus (e.g. derivatives). Introductory material covering what is assumed is included on-line.
Aims
- Introduce concepts of programming in key areas of Medical Image Computing
- Introduce key mathematical and statistical concepts and methods appropriate to the quantitative analysis of medical images.
Learning outcomes
- Understand the key mathematical concepts and computing tools for extracting information from medical images.
- Have developed skills in evaluation of algorithms for the purposes of understanding publications in this area and so contribute to the available pool of new researchers.
Teaching and learning methods
12 3-hour sessions, each of which will involve a mixture of lectures, problem solving and in-class practical programming.
Exercises will be given at the end of each lecture. All material will be made available via Blackboard.
Knowledge and understanding
- Become familiar with a range of mathematical techniques for optimisation, classification, modelling and image analysis
Intellectual skills
- Selecting the most appropriate software tools to solve a problem
- Data analysis and reporting of results
Practical skills
- Familiarity with widely used mathematical techniques and Python implementations thereof.
- Python programming skills
Employability skills
- Analytical skills
- Problem solving
- Written communication
- Other
- Programming (Python) Skills
Assessment methods
| Method | Weight |
|---|---|
| Other | 40% |
| Written exam | 60% |
Two Python programming assignments (20% each)
One final written exam (60%)
Feedback methods
- Formal summative assessments
- Real time educative formative assessments during practical classes
Study hours
| Independent study hours | |
|---|---|
| Independent study | 150 |
Teaching staff
| Staff member | Role |
|---|---|
| Timothy Cootes | Unit coordinator |