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Deep learning the chemopreventive action of NSAIDs in Barrett’s oesophagus

  • Full or part time
  • Application Deadline
    Friday, March 29, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Barrett’s oesophagus is a common premalignant condition of the lower oesophagus. The associated cancer, oesophageal adenocarcinoma, is a devastating disease with an average five-year survival of only 15%. Lacking effective strategies to predict and prevent the progression to EAC, significant time and resources are spent on patients who will never progress, while failing to identify many patients who will develop subsequent disease with significant morbidity and mortality.

To develop novel approaches for the study of Barrett’s esophagus, this project will use deep learning at the forefront of artificial intelligence for analysing histology images of BE. There are three components in this PhD project: 1) Develop a computer vision pipeline for analysing routine Barrett’s histology samples using deep learning; 2) Characterize the spatio-temporal changes in Barrett’s histology over drug use and 3) Combine with genomics using bioinformatics.

This project is driven by the urgent clinical need for greater understanding of driving factors for Barrett’s oesophagus progression. It aims to bring together digital pathology, artificial intelligence, and integrative bioinformatics, which could have direct translational impact. It will contribute to the scientific understanding of Barrett’s esophagus and help accelerate the integration of digital biomarker with clinical practice in the future.

The student will develop transferable skills and full competency in programming in Python and R, computer vision, and machine learning. Specifically, he/she will acquire expertise in the development of deep learning algorithms and digital pathology software, experience in applying statistical methods to cancer data, a deep understanding of pathology and neoplastic progression, and excel at working in a highly translational and collaborative research.

Download a PDF of the complete project proposal:

Candidate profile

Candidates must have a first class or upper second class honours BSc Honours/MSc or equivalent in Computer Science, Bioinformatics, or equivalent disciplines.

How to apply

Full details about these studentship projects, and the online application form, are available on our website, at: Applications for all projects should be made online. Please ensure that you read and follow the application instructions very carefully.

Closing date: Friday 29th March 2019

Please apply via the ICR vacancies web portal:

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