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.
| Comments: | IJCAI Accept |
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.23183 [eess.IV] |
| (or arXiv:2605.23183v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23183 arXiv-issued DOI via DataCite (pending registration) |
From: Pengfei Song Sd [view email]
[v1]
Fri, 22 May 2026 03:05:34 UTC (2,190 KB)
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