A deep learning model for efficient end-to-end stratification of thrombotic risk in left atrial appendage
Qi Gao(Zhejiang University), Zhe Zheng(Chinese Academy of Medical Sciences & Peking Union Medical College), He Li(University of Georgia), Hongguang Fan(Chinese Academy of Medical Sciences & Peking Union Medical College), Jianghong Qian, Xingli Liu, Shengze Cai(ZheJiang Institute For Food and Drug Control)
Cited by 13
Related Papers
Physics-informed neural networks (PINNs) for fluid mechanics: a review
|Acta Mechanica Sinica|2021|1.8k
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
|Journal of Computational Physics|2020|1.1k
Physics-Informed Neural Networks for Heat Transfer Problems
|Journal of Heat Transfer|2021|1.1k
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
|Journal of Fluid Mechanics|2021|333
Dense motion estimation of particle images via a convolutional neural network
|Experiments in Fluids|2019|206