构建可解释的个体化医学图谱，同时捕捉变量影响与不确定性。
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
- 基于神经加法模型，解耦协变量与空间变异信息
- 支持个体预测、群体趋势分析及不确定性估计
- 适合需要可解释性的医疗数据分析场景
本研究旨在为医学等复杂依赖关系的高维数据集开发原则性方法，以揭示个体与群体层面的变异。我们提出\texttt{LucidAtlas}，一种能表征空间变化信息、捕捉协变量影响及群体不确定性的通用图谱表示方法。该方法具备协变量解释、个体化预测、群体趋势分析和不确定性估计能力，且可融入先验知识。针对神经加法模型在处理相关协变量时的信任度问题，我们引入边缘化策略，解释单个预测因子对模型输出（图谱）的影响。通过两个医学数据集验证了方法的泛化能力。结果表明，具有内置可解释性的模型在推动科学发现中至关重要。代码将在论文接受后公开。
原文摘要 · Abstract (English)
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop $\texttt{LucidAtlas}$, an approach that can represent spatially varying information, and can capture the influence of covariates as well as population uncertainty. As a versatile atlas representation, $\texttt{LucidAtlas}$ offers robust capabilities for covariate interpretation, individualized prediction, population trend analysis, and uncertainty estimation, with the flexibility to incorporate prior knowledge. Additionally, we discuss the trustworthiness and potential risks of neural additive models for analyzing dependent covariates and then introduce a marginalization approach to explain the dependence of an individual predictor on the models' response (the atlas). To validate our method, we demonstrate its generalizability on two medical datasets. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. Our code will be publicly available upon acceptance.
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