{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:03Z","timestamp":1758672903795,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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<\/jats:p>","DOI":"10.24963\/ijcai.2025\/540","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"4851-4859","source":"Crossref","is-referenced-by-count":0,"title":["Pixel-wise Divide and Conquer for Federated Vessel Segmentation"],"prefix":"10.24963","author":[{"given":"Tian","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Wenke","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Zekun","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"He","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Wenhui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Mang","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]},{"given":"Yongchao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:18Z","timestamp":1758627258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/540"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/540","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}