A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification

• URL: http://arxiv.org/abs/2504.16520v1
• Date: Wed, 23 Apr 2025 08:45:23 GMT
• Title: A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification
• Authors: Wenwei Li, Liyi Cai, Wu Chen, Anan Li,
• Abstract summary: 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.<n> Experiments on two-photon and fMOST datasets demonstrate superior Top-K accuracy and recall compared to existing methods.
• Score: 1.3472715366596661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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