采用多尺度交叉轴注意力融合网络的咽喉疾病影像分类算法

胡延苏; 王远峥; 侯瑾

doi:10.7652/xjtuxb202606022

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采用多尺度交叉轴注意力融合网络的咽喉疾病影像分类算法

更新时间：2026-06-12

- 采用多尺度交叉轴注意力融合网络的咽喉疾病影像分类算法
- Image Classification Algorithm for Laryngeal Disease Based on a Multi-Scale Cross-Axis Attention Fusion Network
- 西安交通大学学报 2026年60卷第6期页码：256-266
- 作者机构：
  
  长安大学电子与控制工程学院，710064，西安
  西安交通大学第二附属医院，710114，西安
  陕西省耳鼻咽喉疾病精准诊疗重点实验室，710114，西安
- 作者简介：
  
  作者简介：胡延苏（1985—），女，副教授，硕士生导师；
  侯瑾（通信作者），女，副主任医生。
- 基金信息：
  
  陕西省自然科学基金资助项目(2025JC-YBMS-704;2024JC-YBQN-0889;2021JQ-419)
- DOI：10.7652/xjtuxb202606022
  中图分类号： TP391.4
- 收稿：2025-10-24，
  
  纸质出版：2026-06-10
- 稿件说明：
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胡延苏, 王远峥, 侯瑾. 采用多尺度交叉轴注意力融合网络的咽喉疾病影像分类算法[J]. 西安交通大学学报, 2026,60(6):256-266.

HU Yansu, WANG Yuanzheng, HOU Jin. Image Classification Algorithm for Laryngeal Disease Based on a Multi-Scale Cross-Axis Attention Fusion Network[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 256-266.
胡延苏, 王远峥, 侯瑾. 采用多尺度交叉轴注意力融合网络的咽喉疾病影像分类算法[J]. 西安交通大学学报, 2026,60(6):256-266. DOI： 10.7652/xjtuxb202606022.

HU Yansu, WANG Yuanzheng, HOU Jin. Image Classification Algorithm for Laryngeal Disease Based on a Multi-Scale Cross-Axis Attention Fusion Network[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 256-266. DOI： 10.7652/xjtuxb202606022.

摘要

针对咽喉疾病计算机辅助诊断中存在的病灶视角单一、分类精度不足及依赖主观经验判断等问题，提出基于多尺度交叉轴注意力融合网络的咽喉疾病影像算法。引入多尺度交叉轴注意力机制，通过跨空间维度的自适应特征加权，实现细粒度病理特征的精准定位。构建双流协同学习框架，捕获全局解剖特征并聚焦局部上下文信息，实现多视角特征提取。设计特征耦合模块避免简单拼接导致的信息冲突与语义错位，实现特征空间的优化、局部-全局表征的深度整合。研究结果表明：相比于现有的MVT-OFML模型，所提算法在自制单视角、低对比度咽喉影像数据集上的准确性、精确度、召回率、F1分数、特异度可分别获取1.76%、1.56%、3.34%、2.60%、1.10%的性能提升。同时，特征可视化分析揭示了融合网络的决策机制具备显著可解释性，关键病理区域激活强度分布与临床诊断标准保持高度一致。该研究可为智能化辅助诊断系统的开发提供技术支撑。

Abstract

To address limitations in computer-aided diagnosis of laryngeal disease，such as singleview lesion presentation，insufficient classification accuracy，and reliance on subjective experience-based judgment，an image classification algorithm for laryngeal disease based on a multi-scale cross-axis attention fusion network was proposed. A multi-scale cross-axis attention mechanism was introduced to enable precise localization of fine-grained pathological features through adaptive feature weighting across spatial dimensions.A dual-stream collaborative learning architecture was constructed to capture global anatomical structures while focusing on local contextual information，facilitating multi-view feature extraction.A feature coupling module was designed to prevent information conflict and semantic misalignment caused by simple concatenation，optimizing the feature space and deeply integrating local and global representations.Study results showed that，on an in-house single-view，low-contrast laryngeal image dataset，compared with the existing MVT-OFM Lmodel，improvements in accuracy，precision，recall，F1 score，and specificity of 1.76%，1.56%，3.34%，2.60%，and 1.10%，respectively，were achieved.Also，feature visualization analysis revealed that the fusion network’s decision mechanism exhibited noticeable interpretability，with the distribution of activation intensities in key pathological regions being highly consistent with clinical diagnostic criteria.This study is expected to provide technical support for the development of intelligent computer-aided diagnosis systems.

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