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An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data

arXiv 2407.11500 ↗First posted 16 Jul 2024cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 16 Jul 2024.

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References · 18

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6 more
  1. 2024Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planescited by 17
  2. 2017Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networkscited by 16
  3. 2023FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networkscited by 8
  4. 2021Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Networkcited by 6
  5. 2019Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray imagescited by 2
  6. 2021Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasetscited by 2

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