Lucas Farndale

Thesis title: Self-Supervised Machine Learning as a Tool to Measure Cancer Prognosis

Precision Medicine DTP

Year of study: 1

  • University of Glasgow
  • Cancer Research UK Beatson Institute

Contact details

PhD supervisors:

Address

Street

R06
Cancer Research UK Beatson Institute
Switchback Rd
Bearsden

City
Glasgow
Post code
G61 1BD

Biography

Qualifications

MSci Mathematics - University of Glasgow (2022)

First Class Honours

Undergraduate teaching

2022/23

Lectures

  • Machine Learning & Artificial Intelligence for Data Scientists (Masters)

2021/22

Tutorials

  • Maths 4H/5E Numerical Methods (Honours/Masters)
  • Maths 2A, 2C, 2D
  • Maths 1

2020/21

Tutorials

  • Science Fundamentals 1X, 1Y
  • Widening Participation Summer School (Maths)

Research summary

  • Deep Learning
  • Self-Supervised Learning
  • Digital Pathology
  • Graph Neural Networks
  • Mathematical Modelling (particularly agent-based models)

Current research interests

Predicting the prognosis of a patient with potentially cancerous growth is extremely difficult. Classical supervised machine learning requires large datasets with pre-scored ground truth labels, however these simply do not exist for many cancers and pre-cancerous growths, such as colorectal polyps. My work uses a set of emerging but relatively untested self-supervised machine learning techniques to investigate whether there exist as yet undiscovered features in pathology stains which can be used to predict cancer prognosis. These techniques require far less data than supervised methods, and no data labelling, leaving them free from human preconception, error, and bias. My work focuses mainly on cancer, however, the methods developed are broadly applicable to other areas of image analysis, both in medicine and further afield. I'm also working on the integration of multiple different data modalities, particularly -omics data, and methods of extracting information from digital pathology slides at different magnifications.
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