CellTypeGraph: A New Geometric Computer Vision Benchmark
This provides a domain-specific benchmark for plant developmental biology, enabling convenient testing of new geometrical learning methods.
The authors tackled the problem of classifying cells in plant organs by creating a new geometric computer vision benchmark for node classification in geo-referenced graphs, and they found that DeeperGCN performs best among eight tested graph neural network architectures.
Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries. To allow the convenient testing of new geometrical learning methods, the benchmark of Arabidopsis thaliana ovules is made available as a PyTorch data loader, along with a large number of precomputed features. Finally, we benchmark eight recent graph neural network architectures, finding that DeeperGCN currently works best on this problem.