DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
This provides a crucial benchmark for clinical diagnosis of cardiac hypertension, though it is incremental in multimodal fusion.
The paper tackled the problem of diagnosing cardiac hypertension by fusing echocardiographic time series and tabular data, achieving an AUC over 90% on a dataset of 239 patients.
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating secondary modalities by disentangling shared and modality-specific information. Validated on a dataset of 239 patients with echocardiographic time series and tabular records, our model outperforms existing methods, achieving an AUC over 90%. This improvement marks a crucial benchmark for clinical use.