[1]代妮娜,张文君.基于多模态融合技术提升乳腺超声与磁共振成像联合诊断乳腺癌的效能[J].中国医学物理学杂志,2026,43(3):317-320.[doi:DOI:10.3969/j.issn.1005-202X.2026.03.006]
　DAI Nina,ZHANG Wenjun.Improving the diagnostic efficacy of breast cancer by integrating breast ultrasound and magnetic resonance imaging with multimodal fusion technology[J].Chinese Journal of Medical Physics,2026,43(3):317-320.[doi:DOI:10.3969/j.issn.1005-202X.2026.03.006]
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基于多模态融合技术提升乳腺超声与磁共振成像联合诊断乳腺癌的效能()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第3期
页码:
317-320
栏目:
医学人工智能
出版日期:
2026-03-27

文章信息/Info

Title:
Improving the diagnostic efficacy of breast cancer by integrating breast ultrasound and magnetic resonance imaging with multimodal fusion technology
文章编号:
1005-202X（2026）03-0317-04
作者:
代妮娜; 张文君
湖北医药学院附属太和医院超声医学科， 湖北 十堰 442000
Author(s):
DAI Nina;  ZHANG Wenjun
Department of Ultrasound Medicine, Taihe Hospital Affiliated to Hubei University of Medicine, Shiyan 442000, China
关键词:
乳腺癌; 多模态融合; 乳腺超声; 磁共振成像
Keywords:
Keywords: breast cancer multimodal fusion breast ultrasound magnetic resonance imaging
分类号:
R318；R816
DOI:
DOI:10.3969/j.issn.1005-202X.2026.03.006
文献标志码:
A
摘要:
目的：为提升基于多模态融合技术的乳腺超声与磁共振成像（MRI）联合诊断乳腺癌的效能，提出一种基于深度残差网络ResNet-101的多模态数据融合方法，并评估不同模型在多模态数据融合下的表现。方法：选取154例患者的乳腺超声和MRI图像进行数据融合与分析。通过超声与MRI的联合检查数据进行多模态融合，并利用深度残差网络模型进行分类和诊断，最终比较各模型的诊断效能。结果：ResNet-101方法在多模态融合数据的乳腺癌诊断中表现最佳，显著高于单一模态及其他模型的表现。与传统的卷积神经网络和ResNet50模型相比，该方法在诊断准确率、敏感性和特异性等指标上均有显著提升。结论：多模态融合技术能有效提升乳腺癌的诊断效能，特别是深度残差网络ResNet-101模型，显著提高诊断的准确率和鲁棒性，表明多模态数据融合技术在乳腺癌诊断中具有重要的应用前景。
Abstract:
Abstract: Objective To propose a multimodal data fusion method based on the deep residual network ResNet-101 for enhancing the diagnostic efficiency of breast ultrasound combined with magnetic resonance imaging (MRI) in breast cancer based on multimodal fusion technology, and further evaluate the performance of various models under multimodal data fusion. Methods The breast ultrasound and MRI images were collected from 154 patients for data fusion and analysis. Multimodal fusion was performed using the ultrasound and MRI data, and the deep residual network models were utilized for classification and diagnosis. Finally, the diagnostic performance of each model was compared. Results The ResNet-101 method demonstrated the best performance in breast cancer diagnosis using multimodal fusion data, which was significantly superior to that of single-modal imaging and other models. Compared with traditional convolutional neural network and ResNet50 model, the proposed method significantly improved diagnostic accuracy, sensitivity, and specificity. Conclusion Multimodal fusion technology can effectively enhance the diagnostic efficiency of breast cancer. Specifically, the deep residual network ResNet-101 model significantly improves the accuracy and robustness of diagnosis, indicating that multimodal data fusion technology has important application prospects in breast cancer diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2025-08-21 【基金项目】湖北省科技厅地区联合基金重点项目（2024AFD092） 【作者简介】代妮娜，硕士，副主任医师，研究方向：浅表器官疾病诊断，E-mail: dnnaiia_123@163.com
更新日期/Last Update: 2026-03-30