{"id":13497281,"url":"https://github.com/MrGiovanni/UNetPlusPlus","last_synced_at":"2025-03-28T21:32:22.055Z","repository":{"id":37742765,"uuid":"129793058","full_name":"MrGiovanni/UNetPlusPlus","owner":"MrGiovanni","description":"[IEEE TMI] Official Implementation for UNet++","archived":false,"fork":false,"pushed_at":"2025-01-11T07:26:28.000Z","size":6314,"stargazers_count":2403,"open_issues_count":54,"forks_count":547,"subscribers_count":45,"default_branch":"master","last_synced_at":"2025-03-22T20:05:23.036Z","etag":null,"topics":["medical-imaging","segmentation","unet"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MrGiovanni.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-04-16T19:11:27.000Z","updated_at":"2025-03-22T19:11:28.000Z","dependencies_parsed_at":"2023-02-19T05:01:18.716Z","dependency_job_id":"ee124081-51a2-4a88-8a86-429d896e83d4","html_url":"https://github.com/MrGiovanni/UNetPlusPlus","commit_stats":{"total_commits":174,"total_committers":5,"mean_commits":34.8,"dds":"0.12068965517241381","last_synced_commit":"3f749c308761f25ce5bede4b94b4978c2d38e6b5"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MrGiovanni%2FUNetPlusPlus","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MrGiovanni%2FUNetPlusPlus/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MrGiovanni%2FUNetPlusPlus/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MrGiovanni%2FUNetPlusPlus/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MrGiovanni","download_url":"https://codeload.github.com/MrGiovanni/UNetPlusPlus/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246105531,"owners_count":20724323,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["medical-imaging","segmentation","unet"],"created_at":"2024-07-31T20:00:28.013Z","updated_at":"2025-03-28T21:32:22.026Z","avatar_url":"https://github.com/MrGiovanni.png","language":"Python","readme":"\u003ch1 align=\"center\"\u003eUNet++\u003c/h1\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n![visitors](https://visitor-badge.laobi.icu/badge?page_id=MrGiovanni/UNetPlusPlus)\n[![GitHub Repo stars](https://img.shields.io/github/stars/MrGiovanni/UNetPlusPlus?style=social)](https://github.com/MrGiovanni/UNetPlusPlus/stargazers)\n\n\u003c/div\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"100%\" src=\"Figures/fig_unet++.png\" /\u003e\u003c/p\u003e\n\nUNet++ is a new general purpose image segmentation architecture for more accurate image segmentation. UNet++ consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the redesigned skip pathways, which aim to address two key challenges of the U-Net: 1) unknown depth of the optimal architecture and 2) the unnecessarily restrictive design of skip connections.\n\n## Paper\n\nThis repository provides the official Keras implementation of UNet++ in the following papers:\n\n**UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation** \u003cbr/\u003e\n[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) \u003cbr/\u003e\nArizona State University \u003cbr/\u003e\nIEEE Transactions on Medical Imaging ([TMI](https://ieee-tmi.org/)) \u003cbr/\u003e\n[paper](https://arxiv.org/abs/1912.05074) | [code](https://github.com/MrGiovanni/Nested-UNet)\n\n**UNet++: A Nested U-Net Architecture for Medical Image Segmentation** \u003cbr/\u003e\n[Zongwei Zhou](https://www.zongweiz.com), [Md Mahfuzur Rahman Siddiquee](https://github.com/mahfuzmohammad), [Nima Tajbakhsh](https://www.linkedin.com/in/nima-tajbakhsh-b5454376/), and [Jianming Liang](https://chs.asu.edu/jianming-liang) \u003cbr/\u003e\nArizona State University \u003cbr/\u003e\nDeep Learning in Medical Image Analysis ([DLMIA](https://cs.adelaide.edu.au/~dlmia4/)) 2018. **(Oral)** \u003cbr/\u003e\n[paper](https://arxiv.org/abs/1807.10165) | [code](https://github.com/MrGiovanni/Nested-UNet) | [slides](https://docs.wixstatic.com/ugd/deaea1_1d1e512ebedc4facbb242d7a0f2b7a0b.pdf) | [poster](https://docs.wixstatic.com/ugd/deaea1_993c14ef78f844c88a0dae9d93e4857c.pdf) | [blog](https://zhuanlan.zhihu.com/p/44958351)\n\n## Official implementation\n\n- keras/\n- pytorch/\n\n## Other implementation\n- [[PyTorch](https://github.com/qubvel/segmentation_models.pytorch)] (by Pavel Yakubovskiy)\n- [[PyTorch](https://github.com/4uiiurz1/pytorch-nested-unet)] (by 4ui_iurz1)\n- [[PyTorch](https://towardsdatascience.com/biomedical-image-segmentation-unet-991d075a3a4b)] (by Hong Jing)\n- [[PyTorch](https://github.com/ZJUGiveLab/UNet-Version)] (by ZJUGiveLab)\n- [[PyTorch](https://github.com/MontaEllis/Pytorch-Medical-Segmentation)] (by MontaEllis)\n- [[Keras](https://www.kaggle.com/meaninglesslives/nested-unet-with-efficientnet-encoder)] (by Siddhartha)\n\n\n\n## Citation\nIf you use UNet++ for your research, please cite our papers:\n```\n@article{zhou2019unetplusplus,\n  title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation},\n  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},\n  journal={IEEE Transactions on Medical Imaging},\n  year={2019},\n  publisher={IEEE}\n}\n\n@incollection{zhou2018unetplusplus,\n  title={Unet++: A Nested U-Net Architecture for Medical Image Segmentation},\n  author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},\n  booktitle={Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},\n  pages={3--11},\n  year={2018},\n  publisher={Springer}\n}\n\n@phdthesis{zhou2021towards,\n  title={Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis},\n  author={Zhou, Zongwei},\n  year={2021},\n  school={Arizona State University}\n}\n```\n\n## Acknowledgments\n\nThis research has been supported partially by NIH under Award Number R01HL128785, by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant. 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