用对抗焦点损失提升阿尔茨海默病诊断精度，发现潜在生物标志物。
Normative Modeling for AD Diagnosis and Biomarker Identification
- 在自编码器中嵌入对抗焦点损失，精准捕捉复杂病例特征。
- 在OASIS-3和ADNI数据集上显著优于现有方法，准确识别神经解剖偏差。
- 适合从事神经退行性疾病建模与生物标志物研究的学者使用。
本文提出一种新型规范建模方法（FAAE），结合焦点损失与对抗自编码器，用于阿尔茨海默病（AD）诊断与生物标志物识别。该方法为端到端设计，在自编码器结构中嵌入对抗焦点损失判别器，专门针对复杂难判病例进行优化。首先利用健康对照组（HC）数据构建规范模型，再用于估算AD患者整体及局部脑结构异常程度。在OASIS-3与ADNI数据集上的大量实验表明，该方法显著超越现有最先进方法。不仅简化了检测流程，还深化了对疾病生物标志物潜力的理解。代码已开源： https://github.com/soz223/FAAE。
原文摘要 · Abstract (English)
In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer's Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at \url{https://github.com/soz223/FAAE}.
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