Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.

翻译：膝关节骨关节炎是全球范围内导致残疾的常见原因，由于其影像学标志物细微且进展具有个体化特征，其诊断本身存在复杂性。一种有前景的分类途径是应用深度学习方法；然而，这些技术需要大量多样化的数据集，而医学数据采集限制对此构成了重大挑战。现有实践通常依赖较小数据集和迁移学习，但这种方法常会继承不必要的预学习特征，这些特征可能扰乱分类器的向量空间，从而潜在损害性能。本研究提出了一种创新范式，通过引入自适应方差阈值化结合神经架构搜索来改进训练后的专用分类器。该方法带来了两个关键成果：提升了预训练KOA模型的初始准确率，并将NAS输入向量空间缩减了60倍，从而实现了更快的推理速度和更高效的超参数搜索。我们还将该方法应用于一个为KOA分类训练的外部模型。尽管该模型初始性能良好，但应用本方法后其平均准确率得到提升，使其跻身顶级KOA分类模型前三之列。