用多模态融合模型预测颈椎病术后颈痛恢复，提升治疗精准度。
A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
- 设计自适应双向金字塔差分卷积，融合影像纹理与灰度信息。
- 在MMCSD数据集上预测准确率优于现有方法，显著提升效果。
- 适合临床医生用于术后疼痛管理决策支持。
颈痛是颈椎病的主要症状，但其机制尚不明确，导致治疗效果不确定。为解决影像差异和空间错位带来的多模态特征融合难题，本文提出自适应双向金字塔差分卷积（ABPDC）模块，利用差分卷积在纹理提取和灰度不变性上的优势实现多模态融合，并设计特征金字塔配准辅助网络（FPRAN）缓解结构错位问题。在MMCSD数据集上的实验表明，所提模型在术后颈痛恢复预测上表现更优，消融实验进一步验证了其有效性。
原文摘要 · Abstract (English)
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.
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