2 citations · 3 across the 7 of their papers we have counts for
7 papers
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Bin Pu, Jiewen Yang, Liwen Wang +9
A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently, fetal ultrasound screening is…
CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Manner
Yao Du, Jiarong Guo, Xiaomeng Li
Echocardiography is a vital non-invasive modality for cardiac assessment, with left ventricular ejection fraction (LVEF) serving as a key indicator of heart function. Existing LVEF…
AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography
Jiewen Yang, Taoran Huang, Shangwei Ding +9
Echocardiographers can detect pulmonary hypertension using Doppler echocardiography; however, accurately assessing its progression often proves challenging. Right heart catheteriza…
DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
Yiqun Lin, Jixiang Chen, Hualiang Wang +4
Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significan…
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
Jiewen Yang, Yiqun Lin, Bin Pu +3
Echocardiogram video plays a crucial role in analysing cardiac function and diagnosing cardiac diseases. Current deep neural network methods primarily aim to enhance diagnosis accu…
Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware Generative Adversarial Network
Jiarong Guo, Xiaogang Xu, Hengshuang Zhao
3D-aware Generative Adversarial Networks (3D-GANs) currently exhibit artifacts in their 3D geometrical modeling, such as mesh imperfections and holes. These shortcomings are primar…
Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
Wanqin Ma, Huifeng Yao, Yiqun Lin +2
Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are l…