Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment

Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to automation and enhanced accuracy, foundational models in computer vision often emphasize global context at the expense of local details, which are vital for medical imaging diagnostics. To address this, we harness the Swin Transformer's capacity to discern extended spatial dependencies within images through the hierarchical framework. Our novel contribution lies in refining local feature representations, orienting them specifically toward the final distribution of the classifier. This method ensures that local features are not only preserved but are also enriched with task-specific information, enhancing their relevance and detail at every hierarchical level. By implementing this strategy, our model demonstrates significant robustness and precision, as evidenced by extensive validation of two established benchmarks for Knee OsteoArthritis (KOA) grade classification. These results highlight our approach's effectiveness and its promising implications for the future of medical imaging diagnostics. Our implementation is available on https://github.com/mtliba/KOA_NLCS2024

Automatic diagnosis of knee osteoarthritis severity using Swin transformer

Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict the severity of KOA. Our model uses publicly available radiographic datasets with Kellgren and Lawrence scores to enable early detection and severity assessment. To improve the accuracy of our model, we employ a multi-prediction head architecture that utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel training approach that reduces the data drift between multiple datasets to ensure the generalization ability of the model. The results of our experiments demonstrate the effectiveness and feasibility of our approach in predicting KOA severity accurately.

Transformer with Selective Shuffled Position Embedding using ROI-Exchange Strategy for Early Detection of Knee Osteoarthritis

Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causes decreased mobility in seniors. 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 data itself, which does not substantially increase the data diversity. In this paper, we proposed a novel approach based on the Vision Transformer (ViT) model with Selective Shuffled Position Embedding (SSPE) and a ROI-exchange strategy to obtain different input sequences as a method of data augmentation for early detection of KOA (KL-0 vs KL-2). More specifically, we fixed and shuffled the position embedding of ROI and non-ROI patches, respectively. Then, for the input image, we randomly selected other images from the training set to exchange their ROI patches and thus obtained different input sequences. Finally, a hybrid loss function was derived using different loss functions with optimized weights. Experimental results show that our proposed approach is a valid method of data augmentation as it can significantly improve the model's classification performance.

A Confident Labelling Strategy Based on Deep Learning for Improving Early Detection of Knee OsteoArthritis

Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causes decreased mobility in seniors. The diagnosis provided by physicians is subjective, however, as it relies on personal experience and the semi-quantitative Kellgren-Lawrence (KL) scoring system. KOA has been successfully diagnosed by Computer-Aided Diagnostic (CAD) systems that use deep learning techniques like Convolutional Neural Networks (CNN). In this paper, we propose a novel Siamese-based network, and we introduce a new hybrid loss strategy for the early detection of KOA. The model extends the classical Siamese network by integrating a collection of Global Average Pooling (GAP) layers for feature extraction at each level. Then, to improve the classification performance, a novel training strategy that partitions each training batch into low-, medium- and high-confidence subsets, and a specific hybrid loss function are used for each new label attributed to each sample. The final loss function is then derived by combining the latter loss functions with optimized weights. Our test results demonstrate that our proposed approach significantly improves the detection performance.

Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee OsteoArthritis Classification

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.