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 ± 0.8% on the test set, Semixup had comparable performance - BA of 71 ± 0.8% ( ${p}=0.368$ ) 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.

中文翻译：

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Semixup：根据X射线平片对深部半监督膝关节骨关节炎严重程度分级的内外正则化。

膝骨关节炎（OA）是世界上残疾程度最高的因素之一。这种肌肉骨骼疾病是根据临床症状评估的，通常可以通过射线照相评估来确认。放射科医生进行的这种视觉评估需要经验，并且观察者之间存在中等到高度的变异性。最近的文献表明，深度学习方法可以根据金标准Kellgren-Lawrence（KL）分级系统可靠地执行OA严重性评估。然而，这些方法需要大量的标记数据，这是昂贵的。在这项研究中，我们建议Semixup 算法，一种半监督学习（SSL）方法，以利用未标记的数据。 Semixup依赖于使用流形内和流形外样本的一致性正则化以及内插一致性。在独立的测试集上，在大多数情况下，我们的方法大大优于其他最新的SSL方法。最后，与经过调整的完全监督的基准相比，该基准在测试集上产生了70.9±0.8％的平衡准确度（BA），Semixup 性能相当-BA为71±0.8％（ $ {p} = 0.368 $ ），而所需的标记数据则少6倍。这些结果表明，我们提出的SSL方法可以使用研究设置之外可用的数据集构建全自动OA严重性评估工具。