arXiv：cs.LG· Chihun An, Ikbeom Jang·· 3 小时前
CoPoE：基于可分解疾病坐标专家乘积的缺失模态阿尔茨海默病多模态融合诊断
CoPoE: Multimodal Fusion via Decomposable Disease-Coordinate Product-of-Experts for Missing-Modality Alzheimer's Diagnosis
AI 导读
CoPoE 是一种疾病坐标框架，将多模态证据映射到由遗传风险、分子病理、神经退行和临床分期（R/P/N/S）四个轴组成的结构化潜空间，并用带掩码的专家乘积架构仅融合可用模态，缺失模态不参与融合路径且无需合成插补。
正文
Abstract: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 this https URL.
| Comments: | Accepted at IEEE BIBM 2026 |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.11394 [cs.LG] |
| (or arXiv:2610.11394v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11394 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chihun An [view email]
[v1]
Thu, 8 Oct 2026 07:23:29 UTC (501 KB)
来源：arXiv：cs.LG · arxiv.org