用病理切片图预测癌症生存率，精度超现有方法。
Survival Modeling from Whole Slide Images via Patch-Level Graph Clustering and Mixture Density Experts
- 分区域筛选关键组织，构建空间一致的病变图谱。
- 通过混合高斯模型精准预测生存分布，三组数据集准确率超0.7。
- 适合医学影像分析与肿瘤预后研究者使用。
我们提出一种模块化框架，直接从全切片病理图像（WSIs）中预测癌症特异性生存期。该框架包含四个关键阶段：首先，基于分位数的补丁过滤模块通过分位数阈值筛选具有预后意义的组织区域；其次，图正则化补丁聚类利用k近邻图建模表型层面的变异，保证空间与形态的一致性；第三，层次化特征聚合学习簇内与簇间依赖关系，表征多尺度肿瘤组织结构；最后，专家引导的混合密度模型通过高斯混合估计复杂生存分布，实现精细化风险预测。在TCGA LUAD、TCGA KIRC和TCGA BRCA队列上评估，模型分别取得0.653、0.719和0.733的Cox一致性指数，优于现有基于WSI的生存预测方法。
原文摘要 · Abstract (English)
We propose a modular framework for predicting cancer specific survival directly from whole slide pathology images (WSIs). The framework consists of four key stages designed to capture prognostic and morphological heterogeneity. First, a Quantile Based Patch Filtering module selects prognostically informative tissue regions through quantile thresholding. Second, Graph Regularized Patch Clustering models phenotype level variations using a k nearest neighbor graph that enforces spatial and morphological coherence. Third, Hierarchical Feature Aggregation learns both intra and inter cluster dependencies to represent multiscale tumor organization. Finally, an Expert Guided Mixture Density Model estimates complex survival distributions via Gaussian mixtures, enabling fine grained risk prediction. Evaluated on TCGA LUAD, TCGA KIRC, and TCGA BRCA cohorts, our model achieves concordance indices of 0.653 ,0.719 ,and 0.733 respectively, surpassing existing state of the art approaches in survival prediction from WSIs.
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