Abnormality detection and characterisation in neuroimaging using deep learning

The objective of this project is to develop a decision-making tool that identifies abnormalities on MRI brain scans using deep learning, for triage and identifying false negatives in previously reported scans. The project will use a retrospective dataset of >100,000, minimally processed, MRI scans from three different sites, which have been labelled by a combination of consultant neuroradiologists and a validated NLP algorithm. A classification model has been built and validated on this data, using a supervised CNN (DenseNet-121), with high accuracy (AUC 0.943).

One aim is to extend this work by revisiting some of the automatic NLP labelling. It is currently unclear the optimal way to train and utilise BERT-based models for the automatic labelling of radiology reports, specificially to neuroradiology reports in the UK. Most existing approaches rely on fine-tuning a classification head on large pre-trained language models. Preliminary work seems to suggest that models trained from scratch or those that continue pretraining on neuroradiology reports would outperform these models. Following this, it would be necessary to prove whether improvements in label classification led to a meaningful difference in downstream image classification.

Another aim of this project is to characterise abnormalities by normative modelling using deep learning. Normative modelling seeks to gain a representation of healthy anatomy by training on healthy controls, any significant deviation from which would be considered abnormal. The approach is fundamentally different from supervised CNN approaches, which instead attempts to generate features that best separate healthy and abnormal examinations.