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.

我们提出了一种新颖的细胞至块状图卷积网络 (C2P-GCN)，该方法通过双阶段图形构建，将整个 WSI 的结构数据整合到单个图中，有效地在腺癌病理图像中收集局部结构细节，并在所有块状图像之间建立有意义的连接。C2P-GCN 通过将整个 WSI 的结构数据整合到单个图中，使我们的模型能够使用比最新的结直肠癌模型更少的训练数据，实验证明了我们方法的有效性。