DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 19 Sep 2025.
Builds on
17 of 24 references.
References · 17
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- 2023Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Datacited by 69
- 2023A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnosticscited by 43
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- 2024Fusing Echocardiography Images and Medical Records for Continuous Patient Stratificationcited by 3
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Citing papers per year: 2026: 1.
1 paper.