Geometric Deep Learning for the Assessment of Thrombosis Risk in the Left Atrial Appendage
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-78710-3_61
Reference14 articles.
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3. Cresti, A., et al.: Prevalence of extra-appendage thrombosis in non-valvular atrial fibrillation and atrial flutter in patients undergoing cardioversion: a large transoesophageal echo study. EuroIntervention 15(3), e225–e230 (2019)
4. Di Achille, P., Tellides, G., Figueroa, C.A., Humphrey, J.D.: A haemodynamic predictor of intraluminal thrombus formation in abdominal aortic aneurysms. Proc. Royal Soc. A Math. Phys. Eng. Sci. 470(2172), 20140163 (2014)
5. Fey, M., Lenssen, J.E., Weichert, F., Müller, H.: Splinecnn: fast geometric deep learning with continuous b-spline kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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1. Subject-specific factors affecting particle residence time distribution of left atrial appendage in atrial fibrillation: A computational model-based study;Frontiers in Cardiovascular Medicine;2023-03-13
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