Summary of An Ai System For Continuous Knee Osteoarthritis Severity Grading Using Self-supervised Anomaly Detection with Limited Data, by Niamh Belton and Aonghus Lawlor and Kathleen M. Curran
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
by Niamh Belton, Aonghus Lawlor, Kathleen M. Curran
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed three-stage approach for automated continuous grading of knee osteoarthritis (OA) uses anomaly detection principles to learn a robust representation of healthy knee X-rays and grade disease severity based on distance from normality. The method, which includes self-supervised learning with SS-FewSOME, pseudo labeling, denoising with CLIP, and dual centre representation learning (DCRL), outperforms existing techniques by up to 24% in OA detection and correlates with human expert performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to grade the severity of knee osteoarthritis using X-ray images. The method is designed to be more accurate and less reliant on labeled data than current approaches. It works by first learning what healthy knee X-rays look like, then grading disease severity based on how different an image is from normal. This approach outperforms existing methods in detecting OA and correlates with human expert performance. |
Keywords
* Artificial intelligence * Anomaly detection * Representation learning * Self supervised
