{"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/3d-dda-3d-dual-domain-attention-for-brain","title":"3D-DDA: 3D Dual-Domain Attention for Brain Tumor Segmentation","arxiv_id":null,"date":"2023-10-01","proceeding":"ICIP 2023 10","authors":["Vo-Thanh Hoang-Son","Do Nhu-Tai","Nguyen-Quynh Tram-Tran","Kim","Soo-Hyung"],"abstract":"Accurate brain tumor segmentation plays an essential role in the diagnosis process. However, there are challenges due to the variety of tumors in low contrast, morphology, location, annotation bias, and imbalance among tumor regions. This work proposes a novel 3D dual-domain attention module to learn local and global information in spatial and context domains from encoding feature maps in Unet. Our attention module generates refined feature maps from the enlarged reception field at every stage by attention mechanisms and residual learning to focus on complex tumor regions. Our experiments on BraTS 2018 have demonstrated superior performance compared to existing state-of-the-art methods.","url_abs":"https://ieeexplore.ieee.org/document/10222602","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10222602","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":"3d-dda-3d-dual-domain-attention-for-brain","repo_url":"https://github.com/sowwnn/3DDualDomainAttention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2018","task":"Brain Tumor Segmentation","dataset":"BRATS 2018","model":"3D-DDA","rank_in_archive_order":3,"of":4,"metrics":{"ET":"0.8069","TC":"0.8677","WT":"0.9135"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}