{"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/multiresunet-rethinking-the-u-net","title":"MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation","arxiv_id":"1902.04049","date":"2019-02-11","proceeding":"ScienceDirect 2019 2","authors":["Nabil Ibtehaz","M. Sohel Rahman"],"abstract":"In recent years Deep Learning has brought about a breakthrough in Medical\nImage Segmentation. U-Net is the most prominent deep network in this regard,\nwhich has been the most popular architecture in the medical imaging community.\nDespite outstanding overall performance in segmenting multimodal medical\nimages, from extensive experimentations on challenging datasets, we found out\nthat the classical U-Net architecture seems to be lacking in certain aspects.\nTherefore, we propose some modifications to improve upon the already\nstate-of-the-art U-Net model. Hence, following the modifications we develop a\nnovel architecture MultiResUNet as the potential successor to the successful\nU-Net architecture. We have compared our proposed architecture MultiResUNet\nwith the classical U-Net on a vast repertoire of multimodal medical images.\nAlbeit slight improvements in the cases of ideal images, a remarkable gain in\nperformance has been attained for challenging images. We have evaluated our\nmodel on five different datasets, each with their own unique challenges, and\nhave obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%,\n1.41%, and 0.62% respectively.","url_abs":"http://arxiv.org/abs/1902.04049v1","url_pdf":"http://arxiv.org/pdf/1902.04049v1.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":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/nibtehaz/MultiResUNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/Cassieyy/MultiResUnet3D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/anandkr123/DynamicContentErasure","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/avinash0309/BRAIN-TUMOR-SEGEMENTATION-OF-LOW-GRADE-GLIOMAS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/j-sripad/mulitresunet-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multiresunet-rethinking-the-u-net","repo_url":"https://github.com/nikhilroxtomar/semantic-segmentation-architecture","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image 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":{"syntology_url":"https://syntology.ai/paper/1902.04049","atlas_url":"https://app.syntology.ai/?focus=1902.04049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04049"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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