Cell Nucleus-Graph Convolutional Network Evaluation of
Immunohistochemistry Images of Colorectal Cancer
Abstract
This study proposed a method for interpreting immunohistochemistry (IHC)
images based on a graph convolutional network (GCN). Self-supervised
transfer learning was employed to obtain cell nucleus segmentation
masks, providing effective strong cues for a cell nucleus graph (CN-G).
This study applys a GCN to end-to-end diagnostic classification tasks
for IHC images, fully considering global distribution features and local
details in images. We believe that our study makes a significant
contribution to the literature because the proposed approach ensures
high accuracy in the relevant tasks while addressing the challenges of
the lack of labeled datasets and high number of sample pixels.