用双通道注意力实现跨模态神经元精准匹配
A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification
- 双通道注意力分别捕捉细胞体形态与纤维上下文信息
- 在两光子和fMOST数据集上达到更高识别准确率
- 适合小样本下神经科学跨模态图像匹配任务
在神经科学研究中，实现不同成像模态间的单神经元匹配对于理解神经元结构与功能关系至关重要。然而，模态差异和标注数据有限带来了显著挑战。本文提出一种基于预训练视觉变换器的少样本度量学习方法，引入双通道注意力机制，分别提取细胞体形态特征与纤维上下文特征，并通过门控机制融合输出。为增强模型细粒度区分能力，采用基于MultiSimilarityMiner算法的困难样本挖掘策略，结合Circle Loss函数。在两光子与fMOST数据集上的实验表明，该方法在Top-K准确率与召回率方面均优于现有方法。消融实验与t-SNE可视化验证了各模块有效性。在不同微调策略下，该方法也实现了准确率与训练效率的良好平衡。结果表明，该方法为单细胞级精确匹配与多模态神经成像整合提供了有前景的技术方案。
原文摘要 · Abstract (English)
In neuroscience research, achieving single-neuron matching across different imaging modalities is critical for understanding the relationship between neuronal structure and function. However, modality gaps and limited annotations present significant challenges. We propose a few-shot metric learning method with a dual-channel attention mechanism and a pretrained vision transformer to enable robust cross-modal neuron identification. The local and global channels extract soma morphology and fiber context, respectively, and a gating mechanism fuses their outputs. To enhance the model's fine-grained discrimination capability, we introduce a hard sample mining strategy based on the MultiSimilarityMiner algorithm, along with the Circle Loss function. Experiments on two-photon and fMOST datasets demonstrate superior Top-K accuracy and recall compared to existing methods. Ablation studies and t-SNE visualizations validate the effectiveness of each module. The method also achieves a favorable trade-off between accuracy and training efficiency under different fine-tuning strategies. These results suggest that the proposed approach offers a promising technical solution for accurate single-cell level matching and multimodal neuroimaging integration.
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