一种多视图两模态的先天性心脏病分类系统及其训练方法
By employing modal adaptive coding, probabilistic completion, view-modal alignment fusion, and dynamic topological coupling networks, the problem of missing view modalities in ultrasound classification of congenital heart disease was solved, achieving high accuracy and robust classification that adapts to individualized anatomical variations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHWEST PETROLEUM UNIV
- Filing Date
- 2026-05-15
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for ultrasound classification of congenital heart disease suffer from problems such as missing views and modalities. They lack a unified missing perception mechanism, cannot effectively identify missing states, lack reliable cross-modal completion methods, have difficulty recovering missing features, cannot distinguish between valid and invalid views during fusion, are susceptible to noise interference, cannot adapt to individual differences in cardiac malformations, and have low fusion accuracy.
A modality adaptive encoding network, a probabilistic completion network, a view-modality alignment fusion network, and a dynamic topology coupling network are employed, combined with a confidence-weighted pooling module, to achieve differentiated encoding, probabilistic completion, feature alignment, and dynamic coupling for B-mode and Color Doppler modalities. Confidence-weighted pooling is used to improve classification accuracy.
It effectively improves the discriminative power and classification accuracy of congenital heart disease features, stably restores missing information, corrects feature-level spatial offset, adapts to individualized anatomical variations, enhances the accuracy and robustness of view fusion under pathological conditions, suppresses interference from invalid views, and improves the reliability of classification decisions.
Smart Images
Figure CN122200201B_ABST