{"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/a-probabilistic-u-net-for-segmentation-of","title":"A Probabilistic U-Net for Segmentation of Ambiguous Images","arxiv_id":"1806.05034","date":"2018-06-13","proceeding":"NeurIPS 2018 12","authors":["Simon A. A. Kohl","Bernardino Romera-Paredes","Clemens Meyer","Jeffrey De Fauw","Joseph R. Ledsam","Klaus H. Maier-Hein","S. M. Ali Eslami","Danilo Jimenez Rezende","Olaf Ronneberger"],"abstract":"Many real-world vision problems suffer from inherent ambiguities. In clinical\napplications for example, it might not be clear from a CT scan alone which\nparticular region is cancer tissue. Therefore a group of graders typically\nproduces a set of diverse but plausible segmentations. We consider the task of\nlearning a distribution over segmentations given an input. To this end we\npropose a generative segmentation model based on a combination of a U-Net with\na conditional variational autoencoder that is capable of efficiently producing\nan unlimited number of plausible hypotheses. We show on a lung abnormalities\nsegmentation task and on a Cityscapes segmentation task that our model\nreproduces the possible segmentation variants as well as the frequencies with\nwhich they occur, doing so significantly better than published approaches.\nThese models could have a high impact in real-world applications, such as being\nused as clinical decision-making algorithms accounting for multiple plausible\nsemantic segmentation hypotheses to provide possible diagnoses and recommend\nfurther actions to resolve the present ambiguities.","url_abs":"http://arxiv.org/abs/1806.05034v4","url_pdf":"http://arxiv.org/pdf/1806.05034v4.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":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/SimonKohl/probabilistic_unet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/MIC-DKFZ/probabilistic_unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/Usman-Rafique/Probabilistic_UNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/cviviers/prob_3d_segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/jenspetersen/probabilistic-unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/kilgore92/Probabalistic-U-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/rizalmaulanaa/robustness_of_prob_u_net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/stefanknegt/Probabilistic-Unet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"a-probabilistic-u-net-for-segmentation-of","repo_url":"https://github.com/stefanknegt/probabilistic_unet_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.05034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.05034"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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