{"type":"ScholarlyArticle","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","authors":[{"name":"Pengfei Song","slug":"pengfei-song","given":"Pengfei","family":"Song","affiliation":""},{"name":"Fangjin Liu","slug":"fangjin-liu","given":"Fangjin","family":"Liu","affiliation":""},{"name":"Wenwen Zeng","slug":"wenwen-zeng","given":"Wenwen","family":"Zeng","affiliation":""},{"name":"Yonghuang Wu","slug":"yonghuang-wu","given":"Yonghuang","family":"Wu","affiliation":""},{"name":"Chengqian Zhao","slug":"chengqian-zhao","given":"Chengqian","family":"Zhao","affiliation":""},{"name":"Feiyu Yin","slug":"feiyu-yin","given":"Feiyu","family":"Yin","affiliation":""},{"name":"Xuan Xie","slug":"xuan-xie","given":"Xuan","family":"Xie","affiliation":""},{"name":"Jinhua Yu","slug":"jinhua-yu","given":"Jinhua","family":"Yu","affiliation":""}],"abstract":"Contemporary glioma diagnosis integrates molecular features with histopathology to guide clinical decision-making. However, in clinical settings, divergent imaging protocols result in incomplete MRI sequences, leading to two primary challenges: forcing existing frameworks to discard a large portion of clinical data during training and consequently limiting their clinical applicability. To address these limitations, we propose GMENet, a Generative Mixture of Experts Network for multi-center glioma diagnosis with incomplete imaging sequences. Firstly, we design a Cross-attention-based Gated Generation Module that synthesizes missing sequence features from available sequences via cross-attention and dynamic gating mechanisms, incorporating a cycle-consistency loss to preserve semantic integrity. Secondly, we introduce a Dynamically Weighted Experts Fusion Module that performs mixture-of-experts interaction and confidence-aware fusion over original and synthesized dual-sequence features for multi-task prediction. We evaluate GMENet on a multi-center cohort of 1,241 subjects from four in-house datasets and two public repositories. Experiments show that GMENet expands clinically usable training data by 97%, relative to complete-sequence-only data. Furthermore, it consistently outperforms state-of-the-art methods trained on complete data, demonstrating improved robustness under cross-center distribution shifts. Code is available at: https://github.com/spf-sd/GMENet.","doi":"10.24963/ijcai.2026/765","url":"https://ijcai.org/proceedings/2026/765","pdf":"https://ijcai.org/proceedings/2026/0765.pdf","markdown":"https://ijcai.org/proceedings/2026/765.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/765","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":"AI and Health","track":"AI and Health","number":765,"pages":{"first":6877,"last":6885},"keywords":["Medical diagnosis: Medical diagnosis","Medical imaging: Medical imaging","Precision medicine: Precision medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}