131321 20260316125348.0 doi 10.1007/978-3-031-11203-4_3 sideral 132140 ART-2022-132140 eng Ramón Júlvez, Ubaldo Universidad de Zaragoza LDDMM meets GANs: generative adversarial networks for diffeomorphic registration 2022 Access copy available to the general public Unrestricted The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature on deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the state of the art within the LDDMM paradigm. We have successfully implemented two models with the stationary and the EPDiff-constrained non-stationary parameterizations of diffeomorphisms. Our unsupervised learning approach has shown competitive performance with respect to benchmark supervised learning and model-based methods. info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 0.32 2022 Computer Science (miscellaneous) 2022 Q3 Theoretical Computer Science 2022 Q4 2.2 2022 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion Hernández Giménez, Mónica Universidad de Zaragoza (orcid)0000-0003-1270-5852 Mayordomo Cámara, Elvira Universidad de Zaragoza (orcid)0000-0002-9109-5337 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 13386 (2022), 18-28 Lect. notes comput. sci. Lecture Notes in Computer Science 0302-9743 233048 http://zaguan.unizar.es/record/131321/files/texto_completo.pdf Postprint 1637113 http://zaguan.unizar.es/record/131321/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:131321 articulos driver 2026-03-16-08:29:41 ARTICLE