A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
Claudia Vanea, Campbell, Jonathan, Omri Dodi, Liis Salumäe, Karen S. Meir, Drorith Hochner‐Celnikier, Hagit Hochner, Triin Laisk, Linda M. Ernst, Cecilia M. Lindgren, Christoffer Nellåker · arXiv (Cornell University) · 2022
We introduce a new benchmark dataset, Placenta, for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. This problem is uniquely challenging for graph learning for a few reasons. Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure). Here, we release a dataset consisting of two cell graphs from two placenta histology images totalling 2,395,747 nodes, 799,745 of which have ground truth labels. We present inductive benchmark results for 7 scalable models and show how the unique qualities of cell graphs can help drive the development of novel graph neural network architectures.