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Jierui Qu

2 accepted papers

2026

A DYL-UNET FRAMEWORK BASED ON DYNAMIC LEARNING FOR TEMPORALLY CONSISTENT ECHOCARDIOGRAPHIC SEGMENTATION

ICASSP 2026poster

Accurate segmentation of cardiac anatomy in echocardiography is essential for cardiovascular diagnosis and treatment. Yet echocardiography is prone to deformation and speckle noise, causing frame-to-frame segmentation jitter. Even with high accuracy in single-frame segmentation, temporal instability…

Cited by 0SourcePDFScholar
2026

RL-U2Net: A Dual-Branch UNet with Reinforcement Learning-Assisted Multimodal Feature Fusion for Accurate 3D Whole-Heart Segmentation

AAAI 2026technical

Accurate whole-heart segmentation is a critical component in the precise diagnosis and interventional planning of cardiovascular diseases. Integrating complementary information from modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) can significantly enhance segmentatio

Cited by 0SourcePDFScholar