Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal cancer histology images. Recent graph-based techniques involve dividing whole slide images (WSIs) into smaller or medium-sized patches, and then building graphs on each patch for direct use in training. This method, however, fails to capture the tissue structure information present in an entire WSI and relies on training from a significantly large dataset of image patches. In this paper, we propose a novel cell-to-patch graph convolutional network (C2P-GCN), which is a two-stage graph formation-based approach. In the first stage, it forms a patch-level graph based on the cell organization on each patch of a WSI. In the second stage, it forms an image-level graph based on a similarity measure between patches of a WSI considering each patch as a node of a graph. This graph representation is then fed into a multi-layer GCN-based classification network. Our approach, through its dual-phase graph construction, effectively gathers local structural details from individual patches and establishes a meaningful connection among all patches across a WSI. As C2P-GCN integrates the structural data of an entire WSI into a single graph, it allows our model to work with significantly fewer training data compared to the latest models for colorectal cancer. Experimental validation of C2P-GCN on two distinct colorectal cancer datasets demonstrates the effectiveness of our method.

翻译：基于图的学习方法因其编码组织/器官结构信息的能力，在结直肠癌组织学图像分级中日益受到青睐。当前的图基技术涉及将全切片图像（WSI）分割为中等尺度的补丁，并在每个补丁上构建图以直接用于训练。然而，这种方法无法捕捉整张WSI中的组织结构信息，且依赖于大规模图像补丁数据集进行训练。本文提出了一种新型的细胞到补丁图卷积网络（C2P-GCN），这是一种两阶段图构建方法。第一阶段，基于WSI每个补丁上的细胞组织结构构建补丁级图；第二阶段，通过各补丁间的相似性度量构建图像级图，将每个补丁视为图节点。该图表示随后被输入到基于多层GCN的分类网络中。通过这种双阶段图构建方法，我们的方法有效收集了单个补丁的局部结构细节，并在WSI全局范围内建立了所有补丁间有意义的关联。由于C2P-GCN将整张WSI的结构数据整合到单一图中，与最新的结直肠癌模型相比，我们的模型可显著减少训练数据需求。在两个不同结直肠癌数据集上的实验验证表明了C2P-GCN方法的有效性。