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.

Complementing our Pathways to Impact document, here we state the expected real-world impact, which is of course the leading priority for our industrial partners. Their confidence that the proposed CDT will deliver valuable scientific, engineering and commercial impact is emphasized by their overwhelming financial support (£4.38M from industry in the form of cash contributions, and further in-kind support of £5.56M).

Here we summarize what will be the impacts expected from the proposed CDT.

(1) Impact on People
(a) Students
The CDT will have its major impact on the students themselves, by providing them with new understanding, skills and abilities (technical, business, professional), and by enhancing their employability.
(b) The UK public
The engagement planned in the CDT will educate and inform the general public about the high quality science and engineering being pursued by researchers in the CDT, and will also contribute to raising the profile of this mode of doctoral training -- particularly important since the public have limited awareness of the mechanisms through which research scientists are trained.

(2) Impact on Knowledge
New scientific knowledge and engineering know-how will be generated by the CDT. Theses, conference / journal papers and patents will be published to disseminate this knowledge.

(3) Impact on UK industry and economy
UK companies will gain a competitive advantage by using know-how and new techniques generated by CDT researchers.
Companies will also gain from improved recruitment and retention of high quality staff.
Longer term economic impacts will be felt as increased turnover and profitability for companies, and perhaps other impacts such as the generation / segmentation of new markets, and companies receiving inward investment for new products.

(4) Impact on Society
Photonic imaging, sensing and related devices and analytical techniques underpin many of products and services that UK industry markets either to consumers or to other businesses. Reskilling of the workforce with an emphasis on promoting technical leadership is central to EPSRC's Productive Nation prosperity outcome, and our CDT will achieve exactly this through its development of future industrially engaged scientists, engineers and innovators. The impact that these individuals will have on society will be manifested through their contribution to the creation of new products and services that improve the quality of life in sectors like transport, dependable energy networks, security and communications.

Greater internationalisation of the cohort of CDT researchers is expected from some of the CDT activities (e.g. international summer schools), with the potential impact of greater collaboration in the future between the next generations of UK and international researchers.