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Towards to the interpretable medical imaging analysis

  • Full or part time
  • Application Deadline
    Friday, February 14, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Application details:
Reference number: HF/CO/2020
Start date of studentship: 1 October 2020
Closing date of advert: 14 February 2020
Interview date: 16 March 2020

Primary supervisor: Dr Hui Fang
Secondary supervisor: Dr Gerald Schaefer
Artificial Intelligence (AI) based medical Image analysis has become more and more common to improve the diagnosis accuracy and efficiency in hospitals. However, the lack of interpretability and transparency in many deep learning models has become the barrier to further promote the use of the new techniques. You will have the opportunity to work with AI researchers and clinicians to push the boundary of the next generation automated diagnosis system.
Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.
Find out more:
Loughborough University has a flexible working and maternity/parental leave policy ( and is a Stonewall Diversity Champion providing a supportive and inclusive environment for the LGBT+ community. The University is also a member of the Race Equality Charter which aims to improve the representation, progression and success of minority ethnic staff and students. The School of Science is a recipient of the Athena SWAN bronze award for gender equality.

Full Project Detail:
The development of artificial intelligence (AI) applications has attracted significant investments in a variety of fields, such as digital healthcare, autonomous driving, precision agriculture and the financial sector. Although many of these applications have achieved impressive outcomes, the majority of tools in serious applications require a higher level of interpretability. This is particularly true in healthcare. For example, it is necessary for clinicians to have sufficient confidence when using AI in screening programmes or to support diagnosis.
In this PhD project, the student will develop powerful medical image analysis algorithms, based on recent AI paradigms such as deep learning and attention-based learning, with a focus on interpretability and incorporation of domain-specific knowledge. Potential medical applications include skin lesion analysis for melanoma identification, retinal image analysis, brain lesion analysis, and digital pathology.
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Entry requirements:
Essential qualifications and skills:
• an upper-second class honours bachelor's degree in Computer Science
• Experience or a working knowledge in machine learning, data mining, or relevant AI areas
• Experience or a working knowledge in algorithm development and software engineering
• Programming skill: Python, C++, or Matlab
• Ability to write project reports and make technical presentations to academic research groups.
Preferable attributes and experience:
• Experience in carrying on theoretic study using mathematically sound approaches
• Working knowledge in deep learning and its frameworks
• Excellent written and oral communication skills
• Self-motivated with ability to meet deadlines and achieve technical objectives at a high standard.
• Strong real-world problem-solving skills
Funding information:
This studentship will be awarded on a competitive basis to applicants who have applied to this project and/or any of the advertised projects prioritised for funding by the School of Science.
The 3-year studentship provides a tax-free stipend of £15,009 (2019 rate) per annum (in line with the standard research council rates) for the duration of the studentship, plus tuition fees at the UK/EU rate. This studentship is only available to those who are eligible to pay UK/EU fees.
Contact details:
Name: Dr Hui Fang
Email address:
Telephone number: +44(0)1509 222579

How to apply:

All applications should be made online at Under programme name, select Computer Science.

Please quote reference number: HF/CO/2020

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