整合多模态数据，提升阿尔茨海默病诊断的准确性和鲁棒性。
Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease
- 统一模型融合影像、基因和临床数据，通过交叉注意力与模态丢弃增强泛化能力。
- 在多模态数据上达92.2%准确率，仅用影像数据在ADNI上仍保持70.4%准确率。
- 可识别关键脑区与基因，增强临床可解释性，适合医疗场景部署。
阿尔茨海默病影响全球超5500万人，预计到2050年将翻倍，亟需快速、准确且可扩展的诊断方法。现有方法难以同时满足临床可接受的准确率、跨数据集泛化性、缺失模态鲁棒性及可解释性，制约其临床应用。本文提出OmniBrain，一种融合脑部MRI、放射组学、基因表达和临床数据的多模态框架，采用统一模型结合交叉注意力与模态丢弃机制。在ANMerge数据集上实现92.2±2.4%的准确率，且在仅含MRI的ADNI数据集上仍达70.4±2.7%准确率，优于单模态及已有多模态方法。可解释性分析揭示了具有神经病理学意义的脑区与基因，提升临床可信度。OmniBrain为真实世界阿尔茨海默病诊断提供了一种鲁棒、可解释且实用的解决方案。
原文摘要 · Abstract (English)
Alzheimer's disease affects over 55 million people worldwide and is projected to more than double by 2050, necessitating rapid, accurate, and scalable diagnostics. However, existing approaches are limited because they cannot achieve clinically acceptable accuracy, generalization across datasets, robustness to missing modalities, and explainability all at the same time. This inability to satisfy all these requirements simultaneously undermines their reliability in clinical settings. We propose OmniBrain, a multimodal framework that integrates brain MRI, radiomics, gene expression, and clinical data using a unified model with cross-attention and modality dropout. OmniBrain achieves $92.2 \pm 2.4\%$accuracy on the ANMerge dataset and generalizes to the MRI-only ADNI dataset with $70.4 \pm 2.7\%$ accuracy, outperforming unimodal and prior multimodal approaches. Explainability analyses highlight neuropathologically relevant brain regions and genes, enhancing clinical trust. OmniBrain offers a robust, interpretable, and practical solution for real-world Alzheimer's diagnosis.
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