通过解耦融合表格与超声数据，提升心脏肥厚诊断准确率
DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
- 主模态主导，分步融合表格式与超声时序数据
- 在239例患者上实现超90%的AUC性能
- 适合需要多模态医疗诊断的临床研究者
多模态数据融合是提升医疗诊断效果的关键方法。本文提出一种从主模态出发的非对称融合策略，通过分离共享信息与模态特异性信息来整合次要模态。在包含239名患者的超声时序数据与表格式记录的数据集上验证，所提模型性能优于现有方法，AUC超过90%，为临床应用树立了关键基准。
原文摘要 · Abstract (English)
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.
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