Background: Coronary angiography (CAG) is a cornerstone imaging modality for assessing coronary artery disease and guiding interventional treatment decisions. However, in real-world clinical settings, angiographic images are often characterized by complex lesion morphology, severe class imbalance, label uncertainty, and limited computational resources, posing substantial challenges to conventional deep learning approaches in terms of robustness and generalization.Methods: The proposed framework is built upon a pretrained convolutional neural network to construct a lightweight hybrid neural representation. A selective neural plasticity training strategy is introduced to enable efficient parameter adaptation. Furthermore, a brain-inspired attention-modulated loss function, combining Focal Loss with label smoothing, is employed to enhance sensitivity to hard samples and uncertain annotations. Class-imbalance-aware sampling and cosine annealing with warm restarts are adopted to mimic rhythmic regulation and attention allocation mechanisms observed in biological neural systems.Results: Experimental results demonstrate that the proposed lightweight brain-inspired model achieves strong and stable performance in binary coronary angiography classification, yielding competitive accuracy, recall, F1-score, and AUC metrics while maintaining high computational efficiency.Conclusion: This study validates the effectiveness of brain-inspired learning mechanisms in lightweight medical image analysis and provides a biologically plausible and deployable solution for intelligent clinical decision support under limited computational resources.

翻译：背景：冠状动脉造影（CAG）是评估冠状动脉疾病和指导介入治疗决策的基石成像技术。然而，在实际临床环境中，血管造影图像通常具有复杂的病变形态、严重的类别不平衡、标签不确定性以及有限的计算资源等特点，这对传统深度学习方法的鲁棒性和泛化性构成了重大挑战。方法：所提出的框架基于预训练的卷积神经网络构建轻量级混合神经表示。引入选择性神经可塑性训练策略以实现高效的参数适应。此外，采用一种结合Focal Loss与标签平滑的脑启发注意力调制损失函数，以增强对困难样本和不确定标注的敏感性。采用类别不平衡感知采样及带热重启的余弦退火策略，以模拟生物神经系统中观察到的节律性调节和注意力分配机制。结果：实验结果表明，所提出的轻量级脑启发模型在二分类冠状动脉造影任务中实现了强劲且稳定的性能，在保持高计算效率的同时，获得了具有竞争力的准确率、召回率、F1分数和AUC指标。结论：本研究验证了脑启发学习机制在轻量级医学图像分析中的有效性，并为有限计算资源下的智能临床决策支持提供了一种生物学上合理且可部署的解决方案。