AI 中文总结
针对组织病理学图像分类中图结构不准确的问题，提出$G_2^*$-Net分层图学习框架，采用二阶双层优化与DARTS近似，在三类数据集上验证了有效性。
AI 中文摘要
细胞和组织的空间结构为组织病理学图像提供了重要的诊断线索。尽管基于图的方法可以建模这些关系，但许多方法依赖于固定或启发式的图结构，可能无法准确表示组织的连接性。在这项工作中，我们提出了$G_2^*$-Net，这是一个用于对大规模组织病理学图像（如全切片图像（WSIs）或大感兴趣区域（ROIs））进行分类的优化两级图学习框架。其中，$G_2$表示两级分层图表示，上标*表示从所提框架学习到的优化图像级图结构。该方法首先将每个WSI或大ROI划分为图像块，在每个块内构建细胞级图以捕捉局部组织结构，随后将每个块表示为可学习图像级图中的一个节点。$G_2^*$-Net将图像级图结构学习表述为二阶双层优化问题，通过验证驱动的反馈将图连接性学习与分类器优化分离并耦合。为使该表述在计算上可行，我们采用了受DARTS启发的单步展开近似以实现高效的超梯度估计。在三个不同的组织病理学数据集上的实验验证了所提方法的有效性。
英文摘要
The spatial organization of cells and tissues provides important diagnostic cues in histopathology images. Although graph-based approaches can model these relationships, many rely on fixed or heuristic graph structures that may not accurately represent tissue connectivity. In this work, we propose $G_2^*$-Net, an optimized two-level graph learning framework for classifying large-scale histopathology images, such as whole-slide images (WSIs) or large regions of interest (ROIs). Here, $G_2$ denotes the two-level hierarchical graph representation, and the superscript $*$ indicates the optimized image-level graph structure learned from the proposed framework. The method first divides each WSI or large ROI into image patches, constructs cell-level graphs within each patch to capture local tissue architecture, and then represents each patch as a node in a learnable image-level graph. $G_2^*$-Net formulates image-level graph structure learning as a second-order bilevel optimization problem, separating graph connectivity learning from classifier optimization while coupling them through validation-driven feedback. To make this formulation computationally practical, we adopt a DARTS-inspired one-step unrolled approximation for efficient hypergradient estimation. Experimental validation on three distinct histopathology datasets demonstrates the effectiveness of our proposed method.
CommentsAccepted at ICMLA 2026
