  The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA)
ordinal grading systems has been a subject of on-going debate and concern.
Existing automated solutions are trained to emulate these imperfect systems,
whilst also being reliant on large annotated databases for fully-supervised
training. This work proposes a three stage approach for automated continuous
grading of knee OA that is built upon the principles of Anomaly Detection (AD);
learning a robust representation of healthy knee X-rays and grading disease
severity based on its distance to the centre of normality. In the first stage,
SS-FewSOME is proposed, a self-supervised AD technique that learns the 'normal'
representation, requiring only examples of healthy subjects and <3% of the
labels that existing methods require. In the second stage, this model is used
to pseudo label a subset of unlabelled data as 'normal' or 'anomalous',
followed by denoising of pseudo labels with CLIP. The final stage involves
retraining on labelled and pseudo labelled data using the proposed Dual Centre
Representation Learning (DCRL) which learns the centres of two representation
spaces; normal and anomalous. Disease severity is then graded based on the
distance to the learned centres. The proposed methodology outperforms existing
techniques by margins of up to 24% in terms of OA detection and the disease
severity scores correlate with the Kellgren-Lawrence grading system at the same
level as human expert performance. Code available at
https://github.com/niamhbelton/SS-FewSOME_Disease_Severity_Knee_Osteoarthritis.
