A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs

Claudia Vanea; Jonathan Campbell; Omri Dodi; Liis Salumäe; Karen Meir; Drorith Hochner-Celnikier; Hagit Hochner; Triin Laisk; Linda M. Ernst; Cecilia M. Lindgren; Christoffer Nellåker

arXiv · 2211.06292

A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs

Claudia Vanea·Jonathan Campbell·Omri Dodi·Liis Salumäe·Karen Meir·Drorith Hochner-Celnikier·Hagit Hochner·Triin Laisk·Linda M. Ernst·Cecilia M. Lindgren·Christoffer Nellåker

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Abstract

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.

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Claudia Vanea, Jonathan Campbell, Omri Dodi, Liis Salumäe, Karen Meir, Drorith Hochner-Celnikier, Hagit Hochner, Triin Laisk, Linda M. Ernst, Cecilia M. Lindgren, Christoffer Nellåker. 2022-11-11. A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs. https://arxiv.org/abs/2211.06292

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