发表机构
Hanyang University; Hankuk University of Foreign Studies（汉阳大学; 韩国外国语大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoPoE是一种疾病坐标专家乘积框架，可在无需合成插补的情况下融合多模态证据，在ADNI数据集的缺失模态诊断任务中取得最优性能，且保留了病理模块的tau相关信号。
AI 中文摘要
多模态阿尔茨海默病（AD）诊断得益于整合异质性的临床、影像、基因组和生物标志物证据，但临床队列常存在不规则的模态缺失问题。现有融合方法要么合成缺失输入，存在引入人工替代物的风险；要么将可用信号汇聚到不可解释的潜在空间。我们提出CoPoE（疾病坐标专家乘积），这是一种疾病坐标框架，可将多模态证据映射到划分为四个不同生物和临床轴的结构化潜在空间：遗传风险（R）、分子病理（P）、神经退行性变（N）和临床分期（S）。每个观测到的模态对完整的RPNS向量参数化一个对角高斯专家，而掩码专家乘积架构仅融合可用模态。因此，缺失模态不会向融合路径添加任何因子，使网络能在RPNS路径中无需合成插补的情况下，为任何非空模态子集保留稳健、可分解的后验。通过在ADNI数据集上进行的广泛缺失模态实验，在共享非PET ADNI嵌入基准下，CoPoE在所有模态上取得最佳性能，且在标准化缺失模态融合基线的15个观测子集评估中获得最高平均AUROC，同时大幅提升了原始概率的预期校准误差（ECE）、布里尔分数（Brier score）和负对数似然（NLL）。此外，PET监督探测显示，在全模态下病理（P）模块内存在证据富集，即使在直接体液生物样本输入被 withheld 时，仍保留tau相关信号。我们的代码可在此https URL获取。
英文摘要
Multimodal Alzheimer's disease (AD) diagnosis benefits from integrating heterogeneous clinical, imaging, genomic, and biomarker evidence, but clinical cohorts frequently suffer from irregular modality missingness. Existing fusion methods often synthesize absent inputs, risking the introduction of artificial surrogates, or pool available signals into uninterpretable latent spaces. We present CoPoE (Disease-Coordinate Product-of-Experts), a disease-coordinate framework that maps multimodal evidence into a structured latent space partitioned into four distinct biological and clinical axes: genetic Risk, molecular Pathology, Neurodegeneration, and clinical Stage (R/P/N/S). Each observed modality parameterizes a diagonal Gaussian expert over the full RPNS vector, and a masked Product-of-Experts architecture fuses only the available modalities. Consequently, absent modalities add no factor to the fusion path, allowing the network to preserve a robust, decomposable posterior for any non-empty modality subset without synthetic imputation in the RPNS path. Through extensive missing-modality experiments on the ADNI dataset, CoPoE achieves the best all-modality performance and the highest mean AUROC across all 15 observed-subset evaluations among standardized missing-modality fusion baselines under a shared non-PET ADNI embedding benchmark, while substantially improving raw-probability ECE, Brier score, and NLL. Furthermore, PET-supervised probing shows evidence enrichment within the pathology (P) block under full modalities, with tau-related signal retained even when direct fluid biospecimen inputs are withheld. Our code is available at https://github.com/labhai/CoPoE.
CommentsAccepted at IEEE BIBM 2026
