{"type":"ScholarlyArticle","title":"Pixel-wise Divide and Conquer for Federated Vessel Segmentation","authors":[{"name":"Tian Chen","slug":"tian-chen","given":"Tian","family":"Chen","affiliation":""},{"name":"Wenke Huang","slug":"wenke-huang","given":"Wenke","family":"Huang","affiliation":""},{"name":"Zhihao Wang","slug":"zhihao-wang","given":"Zhihao","family":"Wang","affiliation":""},{"name":"Zekun Shi","slug":"zekun-shi","given":"Zekun","family":"Shi","affiliation":""},{"name":"He Li","slug":"he-li","given":"He","family":"Li","affiliation":""},{"name":"Wenhui Dong","slug":"wenhui-dong","given":"Wenhui","family":"Dong","affiliation":""},{"name":"Mang Ye","slug":"mang-ye","given":"Mang","family":"Ye","affiliation":""},{"name":"Bo Du","slug":"bo-du","given":"Bo","family":"Du","affiliation":""},{"name":"Yongchao Xu","slug":"yongchao-xu","given":"Yongchao","family":"Xu","affiliation":""}],"abstract":"Accurate vessel segmentation is essential for diagnosing and managing vascular and ophthalmic diseases. Traditional learning-based vessel segmentation methods heavily rely on high-quality, pixel-level annotated datasets. However, segmentation performance suffers significantly when applied in federated learning settings due to vessel morphology inconsistency and vessel-background imbalance. The former limits the ability of models to capture fine-grained vessels, while the latter overemphasizes background pixels and biases the model towards them. To address these challenges, we propose a novel method named Federated Vessel-Aware Calibration (FVAC),  which leverages global uncertainty to provide differentiated guidance for clients, focusing on pixels of various morphologies that are difficult to distinguish. Furthermore, we introduce a foreground-background decoupling alignment strategy that utilizes more stable and balanced global features to mitigate semantic drift caused by vessel-background imbalance in local clients. Comprehensive experiments confirm the effectiveness of our method","doi":"10.24963/ijcai.2025/540","url":"https://ijcai.org/proceedings/2025/540","pdf":"https://ijcai.org/proceedings/2025/0540.pdf","markdown":"https://ijcai.org/proceedings/2025/540.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/540","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":540,"pages":{"first":4851,"last":4859},"keywords":["Computer Vision: CV: Transparency, accountability, fairness and privacy","Computer Vision: CV: Biomedical image analysis","Machine Learning: ML: Federated learning","Computer Vision: CV: Segmentation, grouping and shape analysis"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}