Incrementally semi-supervised classification of arthritis inflammation on a clinical dataset
Résumé
For best medical imaging application results, learning-based approaches such as deep learning necessitate specific, extensive and precise annotations. Outside well-curated public benchmarks, these are rarely available in practice, and so it becomes necessary to use less-than-perfect annotations. One way of compensating for this is the embedding of anatomical knowledge. Complementing this, there is the incremental semi-supervised learning technique, whereby a small amount of annotations can be used to derive more and superior labels.In this article, we illustrate this approach on a deep learning system to help radiologists and rheumatologists finely and interactively assess MRI scans of the sacro-iliac joint in order to correctly diagnose Axial Spondyloarthritis. Our model is trained initially on a relatively small set of images with promising results, on par with expert opinion and generalizable to new datasets.
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