{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gradicon-approximate-diffeomorphisms-via-1","title":"GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Lin Tian","Hastings Greer","François-Xavier Vialard","Roland Kwitt","Raúl San José Estépar","Richard Jarrett Rushmore","Nikolaos Makris","Sylvain Bouix","Marc Niethammer"],"abstract":"    We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer -- GradICON -- results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. The code is available at https://github.com/uncbiag/ICON.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_GradICON_Approximate_Diffeomorphisms_via_Gradient_Inverse_Consistency_CVPR_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gradicon-approximate-diffeomorphisms-via-1","repo_url":"https://github.com/uncbiag/ICON","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"medical-image-registration","task_name":"Medical Image Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}