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Multiscale-temporal Feature Extraction and boundary confusion alleviation for VAG-based fine-grained multi-grade
Yangwuyong Zhang1, Tongjie Pan2, Yalan Ye2
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Background And Objective:
Multi-grade osteoarthritis (OA) deterioration monitoring in the daily paradigm using Vibroarthrography (VAG) is very challenging due to two difficulties: (1) the composition of VAG signals is complex in the daily paradigm where friction is intensified because of weight-bearing movements. (2) VAG signal samples near the decision boundary of adjacent deterioration grades are easy to be misclassified. The majority of existing works only focus on the binary classification of OA, providing inadequate assistance in instructing physicians to develop treatment plans based on the presence or absence of OA. Thus, we propose a novel framework for fine-grained multi-grade OA deterioration monitoring in the daily paradigm.
Methods:
We propose an end-to-end deep learning framework termed Fine-grained Multi-grade OA Deterioration Monitor (FMOADM), which consists of Multiscale-temporal Feature Extraction (MTFE) and Confusion-Free Master-Slave (CF-MS) Classification. Specifically, MTFE is adopted to extract multiscale-temporal discriminative features from the complicated VAG signals. And center loss is introduced by CF-MS to alleviate confusion at the boundary of adjacent deterioration grades in the feature space. Meanwhile, a master-slave structure is proposed for further fine-grained classification, where the master classifier integrates a channel attention mechanism and the slave classifier is designed to update MTFE parameters. As a result, the proposed method ensures fine-grained multi-grade OA monitoring performance via multiscale-temporal discriminative features and boundary confusion alleviation.
Results:
Experimental results on the VAG-OA dataset demonstrate that our framework outperforms counterpart methods in the daily paradigm. The proposed framework achieved 78% in precision, obtaining an 8% improvement over the state-of-the-art method.
Conclusion:
The proposed framework benefits efficient multi-grade OA deterioration monitoring, empowering physicians to develop treatment plans based on fine-grained monitoring results. It takes knee joint health monitoring in daily activities a step further toward feasible.
