Knee OsteoArthritis (KOA) is a widespread musculoskeletal disorder that can severely impact the mobility of older individuals. Insufficient medical data presents a significant obstacle for effectively training models due to the high cost associated with data labelling. Currently, deep learning-based models extensively utilize data augmentation techniques to improve their generalization ability and alleviate overfitting. However, conventional data augmentation techniques are primarily based on the original data and fail to introduce substantial diversity to the dataset. In this paper, we propose a novel approach based on the Vision Transformer (ViT) model with original Selective Shuffled Position Embedding (SSPE) and key-patch exchange strategies to obtain different input sequences as a method of data augmentation for early detection of KOA (KL-0 vs KL-2). More specifically, we fix and shuffle the position embedding of key and non-key patches, respectively. Then, for the target image, we randomly select other candidate images from the training set to exchange their key patches and thus obtain different input sequences. Finally, a hybrid loss function is developed by incorporating multiple loss functions for different types of the sequences. According to the experimental results, the generated data are considered valid as they lead to a notable improvement in the model's classification performance.

翻译：膝关节骨性关节炎（KOA）是一种广泛存在的肌肉骨骼疾病，可严重影响老年人的活动能力。由于数据标注成本高昂，医疗数据不足对有效训练模型构成了重大障碍。目前，基于深度学习的模型广泛采用数据增强技术以提升泛化能力并缓解过拟合。然而，传统数据增强技术主要依赖原始数据，无法为数据集带来显著的多样性。本文提出一种基于视觉Transformer（ViT）模型的新方法，通过原创的选择性混洗位置嵌入（SSPE）与关键斑块交换策略，获取不同输入序列作为数据增强手段，用于KOA早期检测（KL-0与KL-2级）。具体而言，我们分别对关键斑块和非关键斑块的位置嵌入进行固定与混洗操作。然后，针对目标图像，从训练集中随机选取其他候选图像以交换其关键斑块，从而获得不同的输入序列。最后，针对不同类型的序列，通过整合多个损失函数构建混合损失函数。实验结果表明，生成的数据被认为是有效的，能够显著提升模型的分类性能。