TIER-MoE：用于生物医学分类多模态融合的、基于条件模态风险的信任感知专家路由
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
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中文总结 AI 辅助
该研究提出TIER-MoE风险引导子空间混合专家模型，用于生物医学分类多模态融合，可提升预测性能与概率校准，在多数据集上优于现有最优方法且具备强零样本泛化能力。
中文摘要 AI 辅助
多模态融合的价值在于整合互补的证据来源，但更多证据并不总能带来更优的预测效果。近期多模态模型通过更丰富的跨模态交互与样本自适应融合推进了融合技术的发展，但融合过程中分配给模态的权重无法体现该来源是否不可靠、冗余或与专用专家匹配度差。为解决这一局限，我们提出TIER-MoE，一种风险引导的子空间混合专家模型，将样本特定的模态可靠性定义为其单模态预测器预期产生的预测损失，该风险由未针对对应样本训练的模型生成的折外预测学习得到。TIER-MoE将估计的风险与专家特定的子空间兼容性结合，用于稀疏模态-专家路由，同时保留一条始终激活的共享路径以维持多模态互补性。我们在四个公开多模态生物医学数据集上评估TIER-MoE，涵盖阿尔茨海默病状态、皮肤病变恶性程度及视网膜分类任务。结果表明，TIER-MoE在预测性能与概率校准方面优于现有最优方法，Macro-F1与Brier评分均实现持续提升，且对外部队列具有强零样本泛化能力。
英文摘要
The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction. Recent multimodal models have advanced fusion through richer cross-modal interaction and sample-adaptive fusion. However, the influence assigned to a modality during fusion does not reveal whether that source is unreliable, redundant, or poorly matched to a specialized expert. To address this limitation, we introduce TIER-MoE, a risk-guided subspace mixture-of-experts model that defines sample-specific modality reliability as the prediction loss its unimodal predictor is expected to incur. This risk is learned from out-of-fold predictions generated by models that were not trained on the corresponding sample. TIER-MoE combines the estimated risk with expert-specific subspace compatibility for sparse modality-expert routing, while an always-active shared path preserves multimodal complementarity. We evaluate TIER-MoE on four public multimodal biomedical datasets spanning Alzheimer's disease status, skin-lesion malignancy, and retinal classification. Results demonstrate its superiority over state-of-the-art methods in predictive performance and probability calibration, with consistent improvements in Macro-F1 and Brier score and strong zero-shot generalization to an external cohort.
