Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning.

Vibration arthrography (VAG) signals are widely utilized for knee pathology recognition due to their non-invasive and radiation-free nature. While most studies focus on determining knee health status, few have examined using VAG signals to locate knee lesions, which would greatly aid physicians in diagnosis and patient monitoring. To address this, we propose using Multi-Label classification (MLC) to efficiently locate different types of lesions within a single input. However, current MLC methods are not suitable for knee lesion location due to two major issues: 1) the positive-negative imbalance of pathological labels in knee pathology recognition is not considered, leading to poor performance, and 2) sparse label correlations between different lesions cannot be effectively extracted. Our solution is a label autoencoder incorporating a pre-trained model (PTM-LAE). To mitigate the positive-negative disequilibrium, we propose a pre-trained feature mapping model utilizing focal loss to dynamically adjust sample weights and focus on difficult-to-classify samples. To better explore the correlations between sparse labels, we introduce a Factorization-Machine-based neural network (DeepFM) that combines higher-order and lower-order correlations between different lesions. Experiments on our collected VAG data demonstrate that our model outperforms state-of-the-art methods.