{"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/caussl-causality-inspired-semi-supervised","title":"CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Juzheng Miao","Cheng Chen","Furui Liu","Hao Wei","Pheng-Ann Heng"],"abstract":"    Semi-supervised learning (SSL) has recently demonstrated great success in medical image segmentation, significantly enhancing data efficiency with limited annotations. However, despite its empirical benefits, there are still concerns in the literature about the theoretical foundation and explanation of semi-supervised segmentation. To explore this problem, this study first proposes a novel causal diagram to provide a theoretical foundation for the mainstream semi-supervised segmentation methods. Our causal diagram takes two additional intermediate variables into account, which are neglected in previous work. Drawing from this proposed causal diagram, we then introduce a causality-inspired SSL approach on top of co-training frameworks called CauSSL, to improve SSL for medical image segmentation. Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature. We then propose a novel statistical quantification of the uncomputable algorithmic independence and further enhance the independence via a min-max optimization process. Our method can be flexibly incorporated into different existing SSL methods to improve their performance. Our method has been evaluated on three challenging medical image segmentation tasks using both 2D and 3D network architectures and has shown consistent improvements over state-of-the-art methods. Our code is publicly available at: https://github.com/JuzhengMiao/CauSSL.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Miao_CauSSL_Causality-inspired_Semi-supervised_Learning_for_Medical_Image_Segmentation_ICCV_2023_paper.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":"caussl-causality-inspired-semi-supervised","repo_url":"https://github.com/juzhengmiao/caussl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"},{"task_slug":"semi-supervised-medical-image-segmentation","task_name":"Semi-supervised Medical Image Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-medical-image-segmentation-on-3","task":"Semi-supervised Medical Image Segmentation","dataset":"ACDC 10% labeled data","model":"BCPCauSSL","rank_in_archive_order":3,"of":5,"metrics":{"Dice (Average)":"89.66"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-medical-image-segmentation-on-2","task":"Semi-supervised Medical Image Segmentation","dataset":"ACDC 20% labeled data","model":"BCPCauSSL","rank_in_archive_order":3,"of":4,"metrics":{"Dice (Average)":"89.99"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}