Project Abstract
Artificial intelligence in the form of deep learning, for instance using convolutional neural networks, has made a huge impact on medical image analysis. It dominates conference and journal publications and has demonstrated state-of-the-art performance in many benchmarks and applications, outperforming human observers in some situations. But, despite this, adoption of these approaches in routine clinical practice has been very slow. One reason for this is that deep learning models are inefficient and expensive to train, often requiring tens or hundreds of thousands of expertly labelled training images, and many days training on high-end GPU hardware. For medical applications the requirement for so much expertly labelled data is a key challenge. After all, a radiologist (a doctor specially trained to interpret medical images) is able to learn new tasks using a far smaller set of training images. This project will investigate approaches to improve the efficiency of training deep learning models, reducing the size and/or level of detail of the required training set whilst maintaining diagnostic accuracy. This would enable more clinical applications to be developed sooner, driving improved healthcare. In addition, more efficient models may also enable applications to run on lower-end hardware, giving developing countries access to the latest advanced clinical applications.
The researcher will be based within the AI Research Team at Canon Medical Research Europe, in Edinburgh. Canon Medical are one of the largest manufacturers of medical imaging equipment, including X-ray, CT, MRI, nuclear medicine, PET and ultrasound imaging. The AI Research team develop new algorithms for use with Canon’s scanners and other healthcare products to support clinicians to provide the best possible care for their patients. Current state-of-the-art approaches to this problem include concepts such as transfer learning, domain adaptation, and semi-supervised learning. For this project the researcher will be able to apply novel approaches to reduce the model training burden to a number of real-world exemplar medical imaging applications.
Project Essential Criteria
A Masters level degree (MEng, MPhys, MSc) at 2.1 or equivalent.
Desire to work collegiately, be involved in outreach, undertake taught and professional skills study.
Strong physics, mathematics, computer science or similar background
Experience coding algorithms
Project Desirable Criteria
Experience in machine learning/deep learning
Experience in image processing/computer vision.
An interest in healthcare/medical imaging.
