将心电图模型的解释映射到三维解剖空间，提升临床可解释性。
Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
- 用跨模态映射把心电图模型的注意力投射到3D心脏结构上。
- 在20例专家标注数据上，映射后解释的Dice得分达0.56，优于原方法的0.47。
- 适合关注AI可解释性与临床落地的心脏病研究者。
12导联心电图（ECG）深度学习模型虽诊断性能优异，但缺乏临床所需的直观可解释性。标准特征归因方法难以将抽象波形波动与具体解剖病理关联。为此，本文提出一种跨模态方法，将高性能12导联ECG模型的特征归因投影至CineECG三维解剖空间。研究发现，直接训练于CineECG信号的模型准确率下降且归因不一致，而所提映射机制能有效恢复临床相关特征排序。在20例由领域专家标注的基准数据集上，映射后的解释获得0.56的Dice分数，显著高于标准12导联归因的0.47。结果表明，跨模态平均映射能有效过滤归因不稳定性，提升病灶定位精度，融合了标准ECG的诊断能力与解剖可视化直观性。
原文摘要 · Abstract (English)
Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature attribution methods are limited by the inherent difficulty in mapping abstract waveform fluctuations to physical anatomical pathologies. To resolve this, we propose a cross-modal method that projects feature attributions from high-performance 12-lead ECG models onto the CineECG 3D anatomical space. Our study reveals that while models trained directly on CineECG signals suffer from reduced accuracy and incoherent attributions, the proposed mapping mechanism effectively recovers clinically relevant feature rankings. Validated against a ground-truth dataset of 20 cases annotated by domain experts, the mapped explanations yield a Dice score of 0.56, significantly outperforming the 0.47 baseline of standard 12-lead attributions. These findings indicate that cross-modal averaging mapping effectively filters attribution instability and improves the localization of pathological features, combining the diagnostic expressiveness of standard ECG with the intuitive clarity of anatomical visualization.
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