---
title: "IWCNet: Intervention-based Weight Calibration Network for Multi-center Multi-modality MRI Lesion Segmentation"
authors:
  - "Ronghui Qi"
  - "Wenlong Song"
  - "Wanyin Shi"
  - "Chenchu Xu"
doi: "10.24963/ijcai.2026/172"
url: "https://ijcai.org/proceedings/2026/172"
pdf: "https://ijcai.org/proceedings/2026/0172.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1541-1549"
track: "Main Track"
keywords:
  - "Computer Vision: Biomedical image analysis"
  - "Computer Vision: Segmentation, grouping and shape analysis"
---

# IWCNet: Intervention-based Weight Calibration Network for Multi-center Multi-modality MRI Lesion Segmentation

Ronghui Qi, Wenlong Song, Wanyin Shi, Chenchu Xu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1541-1549. DOI: [10.24963/ijcai.2026/172](https://doi.org/10.24963/ijcai.2026/172)

## 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.