A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
This addresses the problem of uncertain treatment outcomes for cervical spondylosis patients, but it is incremental as it builds on existing multimodal fusion techniques.
The paper tackled predicting postoperative neck pain recovery in cervical spondylosis patients by proposing a multimodal cross-view model, achieving superior prediction accuracy compared to existing methods as demonstrated on the MMCSD dataset.
Neck pain is the primary symptom of cervical spondylosis, yet its underlying mechanisms remain unclear, leading to uncertain treatment outcomes. To address the challenges of multimodal feature fusion caused by imaging differences and spatial mismatches, this paper proposes an Adaptive Bidirectional Pyramid Difference Convolution (ABPDC) module that facilitates multimodal integration by exploiting the advantages of difference convolution in texture extraction and grayscale invariance, and a Feature Pyramid Registration Auxiliary Network (FPRAN) to mitigate structural misalignment. Experiments on the MMCSD dataset demonstrate that the proposed model achieves superior prediction accuracy of postoperative neck pain recovery compared with existing methods, and ablation studies further confirm its effectiveness.