mlsanityData to 14 Oct 2026

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

arXiv 2211.06292 ↗First posted 11 Nov 2022cs.LG cs.CV eess.IVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 11 Nov 2022.

Builds on

22 of 41 references.

References · 22

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10 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Robust Subgraph Learning by Monitoring Early Training Representationscited by 0
  2. 2026Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformerscited by 0