More efficient deep learning for medical image analysis

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.

Novelty of Project-The extravagant data and power requirements of current state-of-the-art deep learning algorithms that limit their rapid deployment and wide use are well recognized; reducing these requirements remains a hot research topic.