{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:17:56Z","timestamp":1769851076192,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>We address the challenging issue of deformable registration that robustly and efficiently builds dense correspondences between images. Traditional approaches upon iterative energy optimization typically invoke expensive computational load. Recent learning-based methods are able to efficiently predict deformation maps by incorporating learnable deep networks. Unfortunately, these deep networks are designated to learn deterministic features for classification tasks, which are not necessarily optimal for registration. In this paper, we propose a novel bi-level optimization model that enables jointly learning deformation maps and features for image registration. The bi-level model takes the energy for deformation computation as the upper-level optimization while formulates the maximum \\emph{a posterior} (MAP) for features as the lower-level optimization. Further, we design learnable deep networks to simultaneously optimize the cooperative bi-level model, yielding robust and efficient registration. These deep networks derived from our bi-level optimization constitute an unsupervised end-to-end framework for learning both features and deformations. Extensive experiments of image-to-atlas and image-to-image deformable registration on 3D brain MR datasets demonstrate that we achieve state-of-the-art performance in terms of accuracy, efficiency, and robustness.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/101","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"723-730","source":"Crossref","is-referenced-by-count":17,"title":["Bi-level Probabilistic Feature Learning for  Deformable Image Registration"],"prefix":"10.24963","author":[{"given":"Risheng","family":"Liu","sequence":"first","affiliation":[{"name":"International School of Information Science & Engineering, Dalian University of Technology"},{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"}]},{"given":"Zi","family":"Li","sequence":"additional","affiliation":[{"name":"International School of Information Science & Engineering, Dalian University of Technology"},{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"}]},{"given":"Yuxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"International School of Information Science & Engineering, Dalian University of Technology"},{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"}]},{"given":"Xin","family":"Fan","sequence":"additional","affiliation":[{"name":"International School of Information Science & Engineering, Dalian University of Technology"},{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"}]},{"given":"Zhongxuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province"},{"name":"School of Software Technology, Dalian University of Technology"},{"name":"Institute of Artificial Intelligence, Guilin University of Electronic Technology"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:13:17Z","timestamp":1594246397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/101"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/101","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}