Machine Learning for Imaging

The course 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. The aim is to provide an overview of different approaches of machine learning including supervised and unsupervised techniques with an emphasis on deep learning methods. Applications include image classification, semantic segmentation, object detection and localization, and registration.

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Past Teaching


The course provides students with knowledge of several generally useful advanced algorithms. Topics include Randomized algorithms, String-matching algorithms, Divide & Conquer, Dynamic programming, Greedy algorithms, Graph algorithms, and more.

Medical Image Computing

The course covers the fundamental concepts and methodologies of medical image computing and image analysis and relates those to clinical applications in diagnosis, therapy and intervention. The aim is to provide an overview of the different areas, such as image processing, registration and segmentation, with an emphasis on understanding the theoretical and practical aspects of various methods. The necessary skills will be taught that enable students to work and conduct research in medical image computing.

The course has been discontinued, and is replaced by our new course Machine Learning for Imaging.

HiPEDS Group Projects

The group projects were an integral part of the HiPEDS CDT programme and its ambition to deliver valuable resources for outreach activities. The project work enabled CDT students to be capable of integrating and innovating across multiple layers of the system development stack, and to acquire creativity, communication, team management and presentation skills.

2018 – AdVanScan: Overview | Cameras | Sensors (supported by Royal Mail)
2017 – Bit Radio | micro:brush | Blind Aid | micro:chem | Gaming Controller (supported by Microsoft)
2016 – Protocopier Project website (supported by Dyson)
2015 – Freescale: Documentation | Testing | Simulation | Sensors (supported by MathWorks)
2014 – Code for Life: News article (supported by Ocado)