Datasets › Placenta
Placenta
Placenta is a benchmark dataset for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. 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).
Source: A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
Image Source: https://arxiv.org/pdf/2211.06292v1.pdf
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Node Classification | Placenta | GraphSAGE Accuracy (%) 64.88±0.43 | A New Graph Node Classification Benchmark: Learning... | nellaker-group/placenta | 5 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 3. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs | 1 | 5 | 11 Nov 2022 | ran 0 of 3 samples (3 unverified) |
Dataset loaders archive 2025-07-28
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Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
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Variants archive 2025-07-28
- Placenta
1 variant name, as the archive lists them.
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