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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02256 |
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| _version_ | 1866911134250958848 |
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| author | Shan, Jingyang Yu, Qishuai Liu, Jiacen Zhang, Shaolin Shen, Wen Zhao, Yanxiao Wang, Tianyi Qin, Xiaolin Yin, Yiheng |
| author_facet | Shan, Jingyang Yu, Qishuai Liu, Jiacen Zhang, Shaolin Shen, Wen Zhao, Yanxiao Wang, Tianyi Qin, Xiaolin Yin, Yiheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients Shan, Jingyang Yu, Qishuai Liu, Jiacen Zhang, Shaolin Shen, Wen Zhao, Yanxiao Wang, Tianyi Qin, Xiaolin Yin, Yiheng Computer Vision and Pattern Recognition 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. |
| title | A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02256 |