用选择性推断提升生成式AI在阿尔茨海默病检测中的可靠性
Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis
- 引入选择性推断解决双次使用数据导致的假阳性问题
- 相比传统方法，该模型在控制错误率的同时保持统计功效
- 适合临床医生用于早期诊断与干预决策支持
阿尔茨海默病因机制不明及患者异质性大，诊断困难。神经退行性变化作为临床诊断生物标志物，可通过时间序列MRI进展测量。生成式AI在医学影像异常检测中展现潜力，可用于肿瘤等任务。然而，由于假设检验中的双次使用数据问题，评估此类数据驱动方法的可靠性极具挑战。本文提出基于选择性推断的方法，构建可靠的生成式AI阿尔茨海默病预测模型。实验表明，相较于传统方法出现严重膨胀的p值，选择性推断能在设定显著性水平α下有效控制假发现率，同时保持统计功效。该方法可为临床提供辅助诊断与早期干预支持。
原文摘要 · Abstract (English)
Alzheimer's Disease is challenging to diagnose due to our limited understanding of its mechanism and large heterogeneity among patients. Neurodegeneration is studied widely as a biomarker for clinical diagnosis, which can be measured from time series MRI progression. On the other hand, generative AI has shown promise in anomaly detection in medical imaging and used for tasks including tumor detection. However, testing the reliability of such data-driven methods is non-trivial due to the issue of double-dipping in hypothesis testing. In this work, we propose to solve this issue with selective inference and develop a reliable generative AI method for Alzheimer's prediction. We show that compared to traditional statistical methods with highly inflated p-values, selective inference successfully controls the false discovery rate under the desired alpha level while retaining statistical power. In practice, our pipeline could assist clinicians in Alzheimer's diagnosis and early intervention.
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