Development of AI Image Analysis Techniques for Cervical Cancer

Description

This is a fully funded PhD position (funded by Remark Holding Inc) in applied medical imaging and deep learning, suited to candidates with a medical imaging, biomedical engineering, applied mathematics, computer science, physics or equivalent MSc/BSc degree. 

The aim of this new collaborative project is to take advantage of state-of-the-art machine learning algorithms for the early diagnosis of cervical cancer. Cervical cancer is the fourth most frequent cancer in women representing 7.9% of all female cancers according to World Health Organization (WHO). Over 270,000 patients in the world died from cervical cancer in 2015. Timely and precise diagnosis of cervical cancer is an important real world medical challenge. The successful candidate will develop novel deep-learning image analysis solutions to support the screening of cervical cancer based on an unprecedented large image dataset.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives. The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives. 

Informal enquiries regarding this project should be made to Dr Yalin Zheng (yalin.zheng@liverpool.ac.uk). All general enquiries should be directed to Mrs Sue Jones (iacdpgr@liverpool.ac.uk).

To apply please send your CV and a covering letter to Dr Zheng (yalin.zheng@liverpool.ac.uk) with a copy to iacdpgr@liverpool.ac.uk

 

Availability

Open to EU/UK applicants

Funding information

Funded studentship

The successful candidate should have an Honours Degree (or equivalent) at 2.1 or above in Mathematics, Engineering, Physics or Computer Science. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Matlab, Python or C++), and signal/image processing plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 or above.

A tax-free stipend of around £15,000 per annum will be paid (exact amount to be confirmed), plus UK home tuition fees and research bench fees. Non-EU (overseas) students are welcome to apply, but they will have to be able cover the financial difference between UK and overseas student fee rates.

Supervisors

References

  1. http://www.who.int/cancer/prevention/diagnosis-screening/cervical-cancer/en/
  2. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521 (7553):436-44.
  3. Goodfellow I, Bengio Y, Courville A. Deep learning: MIT Press; 2016.
  4. Shen D, Wu G, Suk H-I. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering. 2017;19 (1):221-48.
  5. Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y. Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry. 2018; 10:87.
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