Knee OsteoArthritis (KOA) is a prevalent musculoskeletal condition that impairs the mobility of senior citizens. The lack of sufficient data in the medical field is always a challenge for training a learning model due to the high cost of labelling. At present, Deep neural network training strongly depends on data augmentation to improve the model's generalization capability and avoid over-fitting. However, existing data augmentation operations, such as rotation, gamma correction, etc., are designed based on the original data, which does not substantially increase the data diversity. In this paper, we propose a learning model based on the convolutional Auto-Encoder and a hybrid loss strategy to generate new data for early KOA (KL-0 vs KL-2) diagnosis. Four hidden layers are designed among the encoder and decoder, which represent the key and unrelated features of each input, respectively. Then, two key feature vectors are exchanged to obtain the generated images. To do this, a hybrid loss function is derived using different loss functions with optimized weights to supervise the reconstruction and key-exchange learning. Experimental results show that the generated data are valid as they can significantly improve the model's classification performance.

翻译：膝骨关节炎（KOA）是一种常见的肌肉骨骼疾病，会影响老年人的活动能力。由于标注成本高昂，医学领域数据不足一直是训练学习模型的挑战。目前，深度神经网络训练高度依赖数据增强来提高模型泛化能力并避免过拟合。然而，现有的数据增强操作（如旋转、伽马校正等）基于原始数据设计，并未显著增加数据多样性。本文提出一种基于卷积自编码器和混合损失策略的学习模型，用于生成早期KOA（KL-0 vs KL-2）诊断的新数据。在编码器和解码器中设计了四个隐藏层，分别表征每个输入的关键特征和无关特征。然后，交换两个关键特征向量以获取生成图像。为此，利用不同损失函数及优化权重推导出混合损失函数，以监督重建和键交换学习。实验结果表明，生成数据有效，能显著提升模型的分类性能。