C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading

arXiv:2403.04962v1 Announce Type: cross
Abstract: 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 …

abstract arxiv building cancer cs.cv cs.lg eess.iv encode graph graph-based graphs images information medium network type