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PhD Studentship in Computer Science: Multimodal Deep Learning for Lymphoma in Older Patients

Newcastle University

Qualification Type: PhD
Location: Newcastle upon Tyne
Funding for: UK Students, EU Students, International Students
Funding amount: 100% home fees covered, and a minimum tax-free annual living allowance of £18,622 (2023/24 UKRI rate)
Hours: Full Time
Placed On: 2nd February 2024
Closes: 15th March 2024
Reference: COMP2158

Award Summary

100% home fees covered, and a minimum tax-free annual living allowance of £18,622 (2023/24 UKRI rate) 

Overview

Lymphomas are a heterogeneous group of cancers, the diagnosis of which is centred on histopathology but requires integration of clinical, genomic, and anatomical data for diagnostic accuracy and optimal classification. Each aspect is methodologically distinct, highly specialised, and labour intensive; manual assimilation of these data remains laborious and error-prone.  

Digital technologies, encompassing artificial intelligence (AI) approaches, provide an opportunity to streamline and enhance diagnosis. We have previously demonstrated the ability of machine learning, trained only upon diagnostic haematoxylin and eosin (H&E) tissue sections, to distinguish 3 lymphoma subtypes from one another and from inflammatory lymph nodes (Tsakiroglou AM). The accuracy exceeded a general pathologist but did not match an expert haematopathologist. Diagnosing all lymphoma subtypes by AI poses a significantly greater challenge, and our model lacked immunophenotypic and non-histopathology data. 

Objectives 

Advances in machine learning have produced frameworks capable of integrating multiple data modalities while minimising human annotation (Zhou, 2023). We will use such methods to incorporate multimodal data to increase the accuracy of classification across the spectrum of lymphoma. These frameworks have hitherto been trained upon limited datasets, and optimising them for imbalanced data in clinical frameworks remains a critical task to investigate (Lipkova, 2022). 

The successful candidate will benefit from a diverse supervisory team and have access to learning opportunities from the School of Computing and Faculty of Medical Sciences. The student will develop skills in machine learning, image analysis and biomedical computational computer science. 

Number Of Awards: 1

Start Date: September 2024 

Award Duration: 3 years

Application Closing Date: 15 March 2024 

Sponsor

NIHR Newcastle Biomedical Research Centre  

Supervisors

Dr Tong Xin 

Dr Chris Carey 

Dr Chris M. Bacon 

Professor Boguslaw Obara 

Eligibility Criteria

You must have or expect to gain a minimum 2:1 Honours degree or international equivalent in a subject relevant to the proposed PhD project. 

The studentship covers home fees (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants will be required to cover the difference between Home and International fees

Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in each subsection.  

International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme. 

How To Apply

Use Apply to Newcastle Portal 

Once registered select ‘Create a Postgraduate Application’. 

Use ‘Course Search’ to identify your programme of study: 

  • Search for the ‘Course Title’ using programme code: 8050F 
  • Research Area: Computing Science  
  • Select PhD Computer Science as programme of study 

You need to provide the following in ‘Further Details’ section: 

  • ‘Personal Statement’ - upload a document/written statement directly in to the application form 
  • When prompted - select ‘Write Proposal’. Type the title of the research project from this advert. You don’t need to upload a research proposal. 
  • Degree transcripts and certificates and, if English is not your first language, a copy of your English language qualification if already completed. 
  • Studentship code COMP2158 in the ‘Studentship/Partnership Reference’ field 

In the ‘Supporting Documentation’ section upload:   

  • your CV 

Contact Details: Dr Tong Xin 

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