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Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification

CVPR 2022 · Graph learning › Graph self-supervised learning · approach: Heterophily and imbalance on graphs · 68 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use node-aligned global-to-local clustering and graph convolutionNAGCN · TCGA-NSCLCAccuracy: 0.874 -> 0.902Helped
use node-aligned global-to-local clustering and graph convolutionNAGCN · TCGA-RCCAccuracy: 0.919 -> 0.954Helped
add global codebook-based node alignment rather than unaligned per-slide sub-bags (ablation)NAGCN · TCGA-NSCLCAccuracy: 0.855 -> 0.896Helped
add local-clustering-based sub-bag sampling rather than random sampling (ablation)NAGCN · TCGA-NSCLCAccuracy: 0.889 -> 0.896Helped
use graph convolution rather than attention aggregation (ablation)NAGCN · TCGA-NSCLCAccuracy: 0.844 -> 0.896Helped
use graph convolution rather than max pooling (ablation)NAGCN · TCGA-NSCLCAccuracy: 0.813 -> 0.896Helped
increase the number of outer-sub-bag edges per node (ablation)NAGCN · TCGA-NSCLCAccuracyHurt
replace ImageNet-pretrained encoder with a self-supervised encoder (ablation)NAGCN · TCGA-NSCLCAccuracy: 0.928Helped
replace ImageNet-pretrained encoder with a self-supervised encoder (ablation)NAGCN · TCGA-NSCLCAUCROC: 0.972Helped

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