{"type":"ScholarlyArticle","title":"IWCNet: Intervention-based Weight Calibration Network for Multi-center Multi-modality MRI Lesion Segmentation","authors":[{"name":"Ronghui Qi","slug":"ronghui-qi","given":"Ronghui","family":"Qi","affiliation":""},{"name":"Wenlong Song","slug":"wenlong-song","given":"Wenlong","family":"Song","affiliation":""},{"name":"Wanyin Shi","slug":"wanyin-shi","given":"Wanyin","family":"Shi","affiliation":""},{"name":"Chenchu Xu","slug":"chenchu-xu","given":"Chenchu","family":"Xu","affiliation":""}],"abstract":"Multi-modality MRI lesion segmentation leverages complementary contrasts across modalities to better characterize lesion morphology and tissue alterations, thereby improving lesion delineation and reducing misdiagnosis. Existing correlation-driven methods estimate fusion weights by converting cross-modality correlation scores into adaptive fusion weights. However, in multi-center settings, domain shift changes cross-modal correlation patterns, which breaks the learned correlation-to-weight mapping and leads to modality-weight bias that amplifies center-specific shortcuts. To address this issue, we propose an Intervention-based Weight Calibration Network (IWCNet) that models domain shift as a controllable embedding and leverages explicit interventions to mitigate its impact on fusion weight estimation. IWCNet constructs original and intervened branches by performing interventions on the domain embedding, measuring inter-branch prediction effects to calibrate fusion weights. IWCNet further introduces Operator-level Domain Modulation (OLDM) to construct structure-consistent domain-variant feature pairs via operator-level domain intervention, and Effect-Driven Consistency Calibration (EDCC) to jointly leverage intervention effects and cross-branch prediction consistency to calibrate weights. Extensive experiments on multi-center multi-modality MRI datasets demonstrate that IWCNet substantially improves segmentation accuracy and robustness under domain shift.","doi":"10.24963/ijcai.2026/172","url":"https://ijcai.org/proceedings/2026/172","pdf":"https://ijcai.org/proceedings/2026/0172.pdf","markdown":"https://ijcai.org/proceedings/2026/172.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/172","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":172,"pages":{"first":1541,"last":1549},"keywords":["Computer Vision: Biomedical image analysis","Computer Vision: Segmentation, grouping and shape analysis"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}