用双分支网络提升超声影像中罕见甲状腺癌的识别准确率
Intelligent Diagnosis Using Dual-Branch Attention Network for Rare Thyroid Carcinoma Recognition with Ultrasound Imaging
- 结合EfficientNet与ViT，分路提取局部与全局特征
- 在2000+患者数据上实现罕见亚型精准识别，召回率显著提升
- 适合医疗AI开发者及放射科医生参考应用
基于超声影像的罕见甲状腺癌分类面临异质性形态特征和数据不平衡的挑战。为此，我们提出一种新型多任务学习框架——通道-空间注意力协同网络（CSASN），该框架采用双分支特征提取器：利用EfficientNet进行局部空间编码，结合ViT实现全局语义建模，并引入级联式通道-空间注意力优化模块。通过残差多尺度分类器与动态加权损失函数，进一步提升分类稳定性和准确性。模型在来自四家临床机构的超过2000名患者的多中心数据集上训练，消融实验表明各模块均显著贡献于性能提升，尤其在滤泡状癌（FTC）和髓样癌（MTC）等罕见亚型识别中表现突出。实验结果表明，相较于单流CNN或Transformer模型，CSASN在类别不平衡条件下实现了更优的精确率与召回率平衡，为人工智能辅助甲状腺癌诊断提供了一种有效策略。
原文摘要 · Abstract (English)
Heterogeneous morphological features and data imbalance pose significant challenges in rare thyroid carcinoma classification using ultrasound imaging. To address this issue, we propose a novel multitask learning framework, Channel-Spatial Attention Synergy Network (CSASN), which integrates a dual-branch feature extractor - combining EfficientNet for local spatial encoding and ViT for global semantic modeling, with a cascaded channel-spatial attention refinement module. A residual multiscale classifier and dynamically weighted loss function further enhance classification stability and accuracy. Trained on a multicenter dataset comprising more than 2000 patients from four clinical institutions, our framework leverages a residual multiscale classifier and dynamically weighted loss function to enhance classification stability and accuracy. Extensive ablation studies demonstrate that each module contributes significantly to model performance, particularly in recognizing rare subtypes such as FTC and MTC carcinomas. Experimental results show that CSASN outperforms existing single-stream CNN or Transformer-based models, achieving a superior balance between precision and recall under class-imbalanced conditions. This framework provides a promising strategy for AI-assisted thyroid cancer diagnosis.
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