Machine learning (ML) and artificial intelligence (AI) is ubiquitous and finds its application in various fields in science and healthcare. In this course we...

Machine Learning in Medical Imaging (MPHY0041)

Machine learning (ML) and artificial intelligence (AI) is ubiquitous and finds its application in various fields in science and healthcare. In this course we will introduce the underlying mathematics and basic concepts of machine learning including regularized linear models, tree-based models, support vector machines, ensemble methods, neural-networks as well as model assessment. The methods are illustrated and introduced using problems relevant to medical imaging: image reconstruction, image enhancement (noise reduction, super-resolution, image quality transfer), modality transfer, image registration and segmentation, and image-based diagnosis (classification and regression). At the end of course, students will have an overview on relevant methods, their advantages and limitations as well as how to apply them in their field.

A video description can be found at the UCL Media Central.

This course introduces students to a range of issues involved in the design, creation, management and use of electronic resources in the humanities. Topics may...

Digital Resources in the Humanities (INST0013)

The focus of this module is to teach you all aspects of the hyperacute stroke unit – both the system and the types of evidence-based treatments delivered...

Treatment (HASU and Service Delivery) (CLNE0026)

The focus of this module is to teach you all aspects of the hyperacute stroke unit – both the system and the types of evidence-based treatments delivered there (eg iv thrombolysis), including contraindications and complications. You are encouraged to consider how ongoing research will change the way stroke is treated in the future. It also covers unusual causes of stroke and their treatment eg vasculitis, spinal cord infarct. It also has a section on stroke in different population groups such as children, young adults and during pregnancy.

The module objectives are: 1. To evaluate a case for thrombolysis with an explanation of the reasons for and against giving treatment 2. List clinical and imaging markers which predict a poor prognosis in intracerebral haemorrhage 3. Evaluate the current evidence for endovascular treatment 4. List which clinical features can be used to define the dying phase in stroke patients 5. To review the evidence for and use of early supported discharge services in stroke and evaluate the implementation challenges of these services