{"month":"10","author":[{"last_name":"Senouf","first_name":"Ortal","full_name":"Senouf, Ortal"},{"full_name":"Vedula, Sanketh","first_name":"Sanketh","last_name":"Vedula"},{"full_name":"Weiss, Tomer","last_name":"Weiss","first_name":"Tomer"},{"last_name":"Bronstein","first_name":"Alexander","orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6"},{"first_name":"Oleg","last_name":"Michailovich","full_name":"Michailovich, Oleg"},{"full_name":"Zibulevsky, Michael","last_name":"Zibulevsky","first_name":"Michael"}],"quality_controlled":"1","type":"conference","_id":"18269","abstract":[{"text":"In the past few years, deep learning-based methods have demonstrated enormous success for solving inverse problems in medical imaging. In this work, we address the following question: Given a set of measurements obtained from real imaging experiments, what is the best way to use a learnable model and the physics of the modality to solve the inverse problem and reconstruct the latent image? Standard supervised learning based methods approach this problem by collecting data sets of known latent images and their corresponding measurements. However, these methods are often impractical due to the lack of availability of appropriately sized training sets, and, more generally, due to the inherent difficulty in measuring the “groundtruth” latent image. In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data. Our method only requiring access to the measurements and the forward model at training. We showcase its effectiveness on inverse problems arising in accelerated magnetic resonance imaging (MRI). ","lang":"eng"}],"conference":{"end_date":"2019-10-17","start_date":"2019-10-13","location":"Shenzhen, China","name":"DART: MICCAI Workshop on Domain Adaptation and Representation Transfer and MIL3ID: International Workshop on Medical Image Learning with Less Labels and Imperfect Data"},"year":"2019","oa_version":"None","volume":11795,"intvolume":"     11795","date_updated":"2025-01-23T14:50:36Z","doi":"10.1007/978-3-030-33391-1_13","scopus_import":"1","date_created":"2024-10-09T07:41:33Z","publication_identifier":{"isbn":["9783030333904"],"eisbn":["9783030333911"],"issn":["0302-9743"],"eissn":["1611-3349"]},"article_processing_charge":"No","publication_status":"published","page":"111 - 119","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","day":"12","extern":"1","title":"Self-supervised learning of inverse problem solvers in medical imaging","publication":"First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019","language":[{"iso":"eng"}],"publisher":"Springer International Publishing","date_published":"2019-10-12T00:00:00Z","citation":{"apa":"Senouf, O., Vedula, S., Weiss, T., Bronstein, A. M., Michailovich, O., &#38; Zibulevsky, M. (2019). Self-supervised learning of inverse problem solvers in medical imaging. In <i>First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019</i> (Vol. 11795, pp. 111–119). Shenzhen, China: Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-33391-1_13\">https://doi.org/10.1007/978-3-030-33391-1_13</a>","ama":"Senouf O, Vedula S, Weiss T, Bronstein AM, Michailovich O, Zibulevsky M. Self-supervised learning of inverse problem solvers in medical imaging. In: <i>First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019</i>. Vol 11795. Springer International Publishing; 2019:111-119. doi:<a href=\"https://doi.org/10.1007/978-3-030-33391-1_13\">10.1007/978-3-030-33391-1_13</a>","ista":"Senouf O, Vedula S, Weiss T, Bronstein AM, Michailovich O, Zibulevsky M. 2019. Self-supervised learning of inverse problem solvers in medical imaging. First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019. DART: MICCAI Workshop on Domain Adaptation and Representation Transfer and MIL3ID: International Workshop on Medical Image Learning with Less Labels and Imperfect Data vol. 11795, 111–119.","short":"O. Senouf, S. Vedula, T. Weiss, A.M. Bronstein, O. Michailovich, M. Zibulevsky, in:, First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Springer International Publishing, 2019, pp. 111–119.","ieee":"O. Senouf, S. Vedula, T. Weiss, A. M. Bronstein, O. Michailovich, and M. Zibulevsky, “Self-supervised learning of inverse problem solvers in medical imaging,” in <i>First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019</i>, Shenzhen, China, 2019, vol. 11795, pp. 111–119.","mla":"Senouf, Ortal, et al. “Self-Supervised Learning of Inverse Problem Solvers in Medical Imaging.” <i>First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019</i>, vol. 11795, Springer International Publishing, 2019, pp. 111–19, doi:<a href=\"https://doi.org/10.1007/978-3-030-33391-1_13\">10.1007/978-3-030-33391-1_13</a>.","chicago":"Senouf, Ortal, Sanketh Vedula, Tomer Weiss, Alex M. Bronstein, Oleg Michailovich, and Michael Zibulevsky. “Self-Supervised Learning of Inverse Problem Solvers in Medical Imaging.” In <i>First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019</i>, 11795:111–19. Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-33391-1_13\">https://doi.org/10.1007/978-3-030-33391-1_13</a>."}}