| 王彤,苏畅,何萍,王心怡,崔立刚,林伟军.基于跨模态注意力机制特征B型超声与弹性超声融合模块联合诊断乳腺良、恶性肿瘤[J].中国医学影像技术,2022,38(12):1862~1866 |
| 基于跨模态注意力机制特征B型超声与弹性超声融合模块联合诊断乳腺良、恶性肿瘤 |
| B-mode ultrasound combined with elastic ultrasound based on feature fusion module of cross-modal attention mechanism for diagnosis of benign and malignant breast tumors |
| 投稿时间：2022-06-16 修订日期：2022-09-25 |
| DOI：10.13929/j.issn.1003-3289.2022.12.021 |
| 中文关键词: 乳腺肿瘤 神经网络,计算机 超声检查 弹性成像技术 |
| 英文关键词:breast neoplasms neural networks, computer ultrasonography elasticity imaging techniques |
| 基金项目: |
| 作者 | 单位 | E-mail | | 王彤 | 中国科学院声学研究所超声学实验室, 北京 100190
中国科学院大学电子电气与通讯工程学院, 北京 100049 | | | 苏畅 | 中国科学院声学研究所超声学实验室, 北京 100190
中国科学院大学电子电气与通讯工程学院, 北京 100049 | | | 何萍 | 北京大学第三医院超声诊断科, 北京 100191 | heping198679@163.com | | 王心怡 | 北京大学第三医院超声诊断科, 北京 100191
北京大学肿瘤医院 北京市肿瘤防治研究所乳腺中心 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 崔立刚 | 北京大学第三医院超声诊断科, 北京 100191 | | | 林伟军 | 中国科学院声学研究所超声学实验室, 北京 100190
中国科学院大学电子电气与通讯工程学院, 北京 100049 | |
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| 中文摘要: |
| 目的 设计跨模态注意力机制特征融合模块，观察其用于B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的价值。方法 收集371例接受常规超声检查及超声弹性成像的女性乳腺肿瘤患者、共466处病灶；按3∶1∶1将466组病灶图像分为训练集（n=280）、验证集（n=93）及测试集（n=93）。采用卷积神经网络分支模型分别提取B型超声图像和弹性超声图像特征，之后以基于跨模态注意力机制的多模态特征融合网络进行特征融合，观察其诊断乳腺良、恶性肿瘤的价值。结果 改进后的DenseNet用于B型超声诊断乳腺良、恶性肿瘤的准确率为88.43%，敏感度为88.96%，特异度为87.31%，其效能略优于改进前。基于跨模态注意机制特征融合的B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的准确率为94.23%，敏感度为95.11%，特异度为93.28%，效能优于决策加权融合模型、直接串联融合模型及单模态模型。结论 跨模态注意力机制特征融合模块可在一定程度上提高B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的效能。 |
| 英文摘要: |
| Objective To design the feature fusion module of cross-modal attention mechanism, and to observe its value for B-mode ultrasound combined with elastic ultrasound in diagnosis of benign and malignant breast tumors. Methods A total of 371 female patients (466 lesions) with breast tumors who underwent conventional ultrasound and ultrasound elastography were enrolled, and the lesion images were divided into training set (n=280), verification set (n=93) and test set (n=93) at the ratio of 3:1:1. The features of B-mode ultrasound images and elastic ultrasound images were extracted with convolutional neural network branch model, then the feature fusion was performed with multi-mode feature fusion network based on cross modal attention mechanism, and its value for diagnosing benign and malignant breast tumors was observed. Results The efficacy of improved DenseNet for B-mode ultrasound diagnosis of benign and malignant breast tumors was slightly better than that before improvement, with accuracy of 88.43%, sensitivity of 88.96% and specificity of 87.31%. The efficacy of B-mode ultrasound combined with elastic ultrasound based on feature fusion module of cross-modal attention mechanism for diagnosing benign and malignant breast tumors was better than decision weighted fusion model, direct serial fusion model and single-mode model, with accuracy of 94.23%, sensitivity of 95.11% and specificity of 93.28%. Conclusion Feature fusion module of cross-modal attention mechanism could improve the value of B-mode ultrasound combined with elastic ultrasound for diagnosis of benign and malignant breast tumors in a certain extent. |
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