COMPGV17 - Computational Modelling for Biomedical Imaging

This database contains the 2017-18 versions of syllabuses. Syllabuses from the 2016-17 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

CodeCOMPGV17 (also taught as COMPM077)
YearMSc
Prerequisites
Term2
Taught ByDanny Alexander, Ivana Drobnjak, Gary Zhang (100%)
AimsTo expose students to the challenges and potential of computational modelling in a key application area. To explain how to use models to learn about the world. To teach parameter estimation techniques through practical examples. To familiarize students with handling real data sets.
Learning OutcomesStudents successfully completing this module should be able to:
  • Understand tha aims of biomedical imaging
  • understand the advantages and limitations of model-based approaches and data-driven approaches
  • Have knowledge of standard techniques in modelling, experimental design and parameter estimation.
  • Understand the challenges of data modelling, experiment design and parameter estimation in practical situations
  • gain knowledge of handling real-world data in computer programs.

Content

The course introduces the basics of mathematical modelling: the distinction between models and the real world; when and how models are useful; advantages and disadvantages of explicit model-based approaches.

The course covers a range of model based approaches to biomedical imaging and basic computer science techniques that underpin them. The intention is to introduce the students to standard techniques of parameter estimation in a hands-on practical way within the context of model-based imaging. The course also gives exposure to common applications and challenges in biomedical imaging.

The content draws from examples at a range of lengthscales from molecular imaging, cellular scales in microscopy, regional scales, whole organ and whole population scales. The course uses each example to introduce both new kinds of model and, more fundamentally, new algorithms and techniques for parameter estimation, optimization, sampling and validation.

Method of Instruction

Lectures and lab classes. 

Assessment

The course has the following assessment components:

  • Coursework (100%)
    • Coursework 1 (35%)
    • Coursework 2 (15%)
    • Group and individual project (50%)

To pass this course, students must:

  • Achieve a mark of 50% overall.

Resources

Reading list available via the UCL Library catalogue.