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.

提出了一种新的方法来改进后训练的专门分类器，在应用于膝关节骨关节炎（KOA）模型时提高了初始准确性，并在NAS输入向量空间上实现了60倍的减少，从而加快了推理速度和超参数搜索的效率，同时将这种方法应用于外部的KOA分类模型，提高了其平均准确率，使其成为前三的KOA分类模型之一。

Adaptive Variance Thresholding: A Novel Approach to Improve Existing
  Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis
  Classification

自适应方差阈值: 改进现有的深度迁移视觉模型并推进膝关节骨关节炎分类