A physics-informed deep learning framework for modeling of coronary in-stent restenosis
Author:
Funder
Deutsche Forschungsgemeinschaft
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Modeling and Simulation,Biotechnology
Link
https://link.springer.com/content/pdf/10.1007/s10237-023-01796-1.pdf
Reference35 articles.
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2. Barlis P, Regar E, Serruys PW, Dimopoulos K, van der Giessen WJ, van Geuns R-JM, Ferrante G, Wandel S, Windecker S, van Es G-A, Eerdmans P, Jüni P, di Mario C (2010) An optical coherence tomography study of a biodegradable vs. durable polymer-coated limus-eluting stent: a leaders trial sub-study. Eur Heart J 31(2):165–176. https://doi.org/10.1093/eurheartj/ehp480
3. Baydin AG, Pearlmutter BA, Radul AA, Siskind JM (2018) Automatic differentiation in machine learning: a survey. J Mach Learn Res 18(1):5595–5637
4. Caiazzo A, Evans D, Falcone J-L, Hegewald J, Lorenz E, Stahl B, Wang D, Bernsdorf J, Chopard B, Gunn J, Hose R, Krafczyk M, Lawford P, Smallwood R, Walker D, Hoekstra A (2011) A complex automata approach for in-stent restenosis: two-dimensional multiscale modelling and simulations. J Computat Sci 2(1):9–17. https://doi.org/10.1016/j.jocs.2010.09.002
5. Ciompi F, Balocco S, Rigla J, Carrillo X, Mauri J, Radeva P (2016) Computer-aided detection of intracoronary stent in intravascular ultrasound sequences. Med Phys 43(10):5616–5625. https://doi.org/10.1118/1.4962927
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