• Prospective Students
    • The Programme
    • How to Apply
    • Project Vacancies
  • Our Partners
    • Academic Profiles
    • Partner Companies
    • Technical Scope
    • Capital Equipment
    • Propose a Project
  • Current Students
    • Our Students
    • Guidelines and Resources
  • News & Events
    • News
    • Events
    • Publications
Menu
  • Prospective Students
    • The Programme
    • How to Apply
    • Project Vacancies
  • Our Partners
    • Academic Profiles
    • Partner Companies
    • Technical Scope
    • Capital Equipment
    • Propose a Project
  • Current Students
    • Our Students
    • Guidelines and Resources
  • News & Events
    • News
    • Events
    • Publications
Project

More efficient deep learning for medical image analysis

TBA

Project Type: EngD

Supervisor: TBA

Website: https://research.eu.medical.canon/

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.

Other Projects

New platforms in simultaneous multi-analyte sensing 🌐

March 5, 2021

Development of Optoelectronic Sensor Systems to Support Materials Life Assessment

January 19, 2021

Laser sources and semiconductor optical amplifiers for free-space orbital angular momentum communication systems 🌐

February 12, 2020

Development of compact remote gas detection devices using chip-scale deep ultra-violet light-emitting diodes and single photon detectors

March 5, 2021
View All Projects

Home » More efficient deep learning for medical image analysis

UoE

EPSRC Centre for Doctoral Training in Applied Photonics

CDT Office: 44 (0)131 451 8229

Twitter

Heriot-Watt University, Edinburgh, Scotland. Scottish Registered charity number: SC000278 | Disclaimer

  • Equality, Diversity and Inclusion Statement
  • Contacts
  • About Us
  • Login
  • Support Staff
Menu
  • Equality, Diversity and Inclusion Statement
  • Contacts
  • About Us
  • Login
  • Support Staff
This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Cookie settingsACCEPT
Privacy & Cookies

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.
Necessary
Always Enabled

Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.

Non-necessary

Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.