Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution
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 f...
| Authors | Karol Dobiczek, Maciej Mozolewski, Szymon Bobek et al. |
|---|---|
| Primary category | eess.IV |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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