mlsanityData to 14 Oct 2026

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences

arXiv 2605.23183 ↗First posted 22 May 2026eess.IV cs.CVcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 17 of its 41 references that are in the map. First posted on arXiv 22 May 2026.

Builds on

17 of 41 references.

References · 17

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  10. 2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 87
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  12. 2024PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Ratescited by 17
5 more
  1. 2021CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translationcited by 15
  2. 2021Semi-supervised learning for medical image classification using imbalanced training datacited by 14
  3. 2025MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessmentcited by 7
  4. 2023Fast Particle-based Anomaly Detection Algorithm with Variational Autoencodercited by 6
  5. 2023Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Lossescited by 2

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