Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs

TL;DR

Semixup introduces a semi-supervised learning method leveraging in- and out-of-manifold samples for accurate knee osteoarthritis severity grading from radiographs, reducing labeled data needs while maintaining high performance.

Contribution

The paper presents Semixup, a novel SSL algorithm that improves OA severity grading by effectively utilizing unlabeled data through consistency regularization with in- and out-of-manifold samples.

Findings

Semixup outperforms other SSL methods on test data.

Achieves comparable accuracy to fully supervised models with less labeled data.

Requires six times fewer labeled samples than baseline.

Abstract

Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiographic assessment. This visual assessment done by a radiologist requires experience, and suffers from moderate to high inter-observer variability. The recent literature has shown that deep learning methods can reliably perform the OA severity assessment according to the gold standard Kellgren-Lawrence (KL) grading system. However, these methods require large amounts of labeled data, which are costly to obtain. In this study, we propose the Semixup algorithm, a semi-supervised learning (SSL) approach to leverage unlabeled data. Semixup relies on consistency regularization using in- and out-of-manifold samples, together with interpolated consistency. On an independent test set, our method significantly outperformed other state-of-the-art SSL methods in most cases. Finally, when compared to a well-tuned fully supervised baseline that yielded a balanced accuracy (BA) of $70.9\pm0.8%$ on the test set, Semixup had comparable performance -- BA of $71\pm0.8%$ $(p=0.368)$ while requiring $6$ times less labeled data. These results show that our proposed SSL method allows building fully automatic OA severity assessment tools with datasets that are available outside research settings.