14 citations · 16 across the 5 of their papers we have counts for
5 papers
Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging
Mengxue Zhang, Qingrui Cai, Yinyin Chen +13
Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning…
DOD-SA: Infrared-Visible Decoupled Object Detection with Single-Modality Annotations
Hang Jin, Chenqiang Gao, Junjie Guo +4
Infrared-visible object detection has shown great potential in real-world applications, enabling robust all-day perception by leveraging the complementary information of infrared a…
Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI
Yirong Zhou, Chengyan Wang, Mengtian Lu +17
In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods…
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
Zhihao Xue, Fan Yang, Juan Gao +5
Three-dimensional coronary magnetic resonance angiography (CMRA) demands reconstruction algorithms that can significantly suppress the artifacts from a heavily undersampled acquisi…
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
Heming Zhu, Yu Cao, Hang Jin +5
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc. Recent advances in lea…