Measuring Cancer Prognosis with Self-Supervised Learning

Studentship strategic priority area: Mathematics, statistics and computation
Keywords: Artificial Intelligence, colon cancer, modelling, metastasis

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. This project will use a set of state-of-the-art self-supervised machine learning techniques to investigate whether there exist as yet undiscovered features in pathology stain 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.
The project will focus on cancer, however, the methods developed will be broadly applicable to other areas of medical imaging. Further work will investigate the integration of multiple different data modalities, particularly -omics data, and methods of extracting information from digital pathology slides at different magnifications.