Machine Learning for Imaging

Module aims

This module covers the fundamental concepts and advanced methodologies of machine learning for imaging and relates those to real-world problems in computer vision and medical image analysis. You will experience different approaches to machine learning including supervised and unsupervised techniques with an emphasis on deep learning methods. Topics include image segmentation, image registration, self-supervised learning, inverse problems (super-resolution, image reconstruction), causality in imaging, and trustworthy machine learning. A key objective is to equip you with the skills needed to work in, and conduct research into, image computing and applied machine learning.

Learning outcomes

Upon successful completion of this module you will be able to:

• select and apply appropriate machine learning methods for solving practical problems in image computing
• implement and assess techniques for image segmentation, image registration, and self-supervised learning
• compare, characterise and quantitatively assess competing approaches to computer vision and image computing
• evaluate the performance of computer vision and image computing algorithms
• analyse critically the limitations of machine learning techniques in the domain of image computing

Module syllabus

• Introduction to machine learning for imaging
• Image segmentation
• Image registration
• Self-supervised representation learning
• Inverse problems (super-resolution, image reconstruction)
• Trustworthy machine learning in imaging (privacy, explainability)
• Causality in imaging

Highly recommended modules: Computer Vision, Introduction to Machine Learning, and Deep Learning.

Teaching methods

This module focuses on learning through doing. Each week a new topic is introduced in a lecture, followed by a hands-on computer-based laboratory sessions with programming exercises in which taught methods and algorithms are implemented and tested on example data from different imaging applications. Support is given by the course leaders and Graduate Teaching Assistants (GTAs) allowing you to get further advice and feedback from experts. The learned material is applied in one coursework which aims at building a substantial real-world imaging application.
 
An online service will be used as a discussion forum for the module.

Assessments

There will one assessed mini-project (coursework) focusing on developing machine learning based computer vision applications. The project is designed to reinforce the material covered in lectures and give you hands-on experience of solving real imaging problems. You work in groups of two. In total the coursework counts for 20% of the marks for the module. There will be a final written exam, testing core knowledge and skills and your ability to transfer what you have learnt to unseen problems. This exam counts for the remaining 80% of the marks for the module.                           
Feedback for the weekly programming exercises and coursework is provided by Q&A sessions during the lectures. You will also receive written feedback for the coursework; this will be returned electronically.

Reading list

Background material

• Pattern recognition and machine learning

  Bishop, Christopher M., author.

  Springer

• Deep Learning Foundations and Concepts

  Christopher M. Bishop , Hugh Bishop

  1st, Springer

• Deep learning

  Goodfellow, Ian, author.

  The MIT Press

• Mathematics for machine learning

  Deisenroth, Marc Peter, author.

  Cambridge University Press

• Medical Image Analysis

  Elsevier Science & Technology

Module leaders

Professor Ben Glocker
Professor Daniel Rueckert