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Knee OsteoArthritis (KOA) is a widespread musculoskeletal disorder that canseverely impact the mobility of older individuals. Insufficient medical datapresents a significant obstacle for effectively training models due to the highcost associated with data labelling. Currently, deep learning-based modelsextensively utilize data augmentation techniques to improve theirgeneralization ability and alleviate overfitting. However, conventional dataaugmentation techniques are primarily based on the original data and fail tointroduce substantial diversity to the dataset. In this paper, we propose anovel approach based on the Vision Transformer (ViT) model with originalSelective Shuffled Position Embedding (SSPE) and key-patch exchange strategiesto obtain different input sequences as a method of data augmentation for earlydetection of KOA (KL-0 vs KL-2). More specifically, we fix and shuffle theposition embedding of key and non-key patches, respectively. Then, for thetarget image, we randomly select other candidate images from the training setto exchange their key patches and thus obtain different input sequences.Finally, a hybrid loss function is developed by incorporating multiple lossfunctions for different types of the sequences. According to the experimentalresults, the generated data are considered valid as they lead to a notableimprovement in the model's classification performance.

Transformer with Selective Shuffled Position Embedding and Key-Patch Exchange Strategy for Early Detection of Knee Osteoarthritis