{"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/deep-bayesian-inversion","title":"Deep Bayesian Inversion","arxiv_id":"1811.05910","date":"2018-11-14","proceeding":null,"authors":["Jonas Adler","Ozan Öktem"],"abstract":"Characterizing statistical properties of solutions of inverse problems is\nessential for decision making. Bayesian inversion offers a tractable framework\nfor this purpose, but current approaches are computationally unfeasible for\nmost realistic imaging applications in the clinic. We introduce two novel deep\nlearning based methods for solving large-scale inverse problems using Bayesian\ninversion: a sampling based method using a WGAN with a novel mini-discriminator\nand a direct approach that trains a neural network using a novel loss function.\nThe performance of both methods is demonstrated on image reconstruction in\nultra low dose 3D helical CT. We compute the posterior mean and standard\ndeviation of the 3D images followed by a hypothesis test to assess whether a\n\"dark spot\" in the liver of a cancer stricken patient is present. Both methods\nare computationally efficient and our evaluation shows very promising\nperformance that clearly supports the claim that Bayesian inversion is usable\nfor 3D imaging in time critical applications.","url_abs":"http://arxiv.org/abs/1811.05910v1","url_pdf":"http://arxiv.org/pdf/1811.05910v1.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":"deep-bayesian-inversion","repo_url":"https://github.com/JamesGlare/Neural-Net-LabView-DLL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"wgan","method_name":"WGAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.05910","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}