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 |
| Offered by | Division of Informatics, Imaging and Data Sciences |
| 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
The unit aims to:
• 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.
On successful completion of the unit, students should
• 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
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
Each session will include
a) Approximately 90 minutes of taught material (also available broken down into 3-10 minute videos on Blackboard) interspersed with mathematical problems illustrating some of the ideas.
b) A hands-on practical section in which it is shown how mathematical ideas introduced in the lectures are implemented using Python libraries. Students should try the examples on their own laptops and are given in-class challenges.
c) A programming challenge on some aspect of the work to be done before the next session (some of these will be assessed)
Knowledge and understanding
A1 Understand key mathematical techniques useful for medical imaging and image analysis, including optimisation, shape/appearance modelling, classification and regression methods
A2 Understand and be able to use optimisation techniques
A3 Understand shape and appearance models
A4 Understand and be able to use classification and regression methods
Intellectual skills
B1 The ability to turn algorithm specifications into code
B2 The ability to choose the most appropriate technique to solve various mathematical problems
Practical skills
C1 To be able to write Python code to solve mathematical problems
C2 To be familiar with key Python libraries (NumPy, SciPy and PyTorch)
Transferable skills and personal qualities
D1 Use originality to solve mathematical problems using software
D2 Familiarity with writing programs using the Python language
Employability skills
- Analytical skills
- Problem solving
- Written communication
- Other
- Programming (Python) Skills
Assessment methods
| Method | Weight |
|---|---|
| Other | 40% |
| Written exam | 60% |
Assessment task | Length | How and when feedback is provided | Weighting within unit (if relevant)
| ||||||
Formative 1 Programming challenge (to demonstrate the student’s ability to solve practical problems by writing Python scripts and to present the results clearly) |
|
|
| ||||||
Summative 1 Programming challenge (to demonstrate the student’s ability to solve practical problems by writing Python scripts and to present the results clearly)
2 Final written Exam (2 hrs) |
Study hours
Teaching staff
| ||||||||