CellTypeGraph: A New Geometric Computer Vision Benchmark

Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherIEEE Computer Society
Pages20865-20875
Number of pages11
ISBN (Electronic)9781665469463
DOIs
StatePublished - 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
Duration: 19 Jun 202224 Jun 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2022-June
ISSN (Print)1063-6919

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2224/06/22

Keywords

  • Datasets and evaluation
  • Deep learning architectures and techniques
  • Machine learning
  • Medical
  • Segmentation
  • biological and cell microscopy
  • grouping and shape analysis

Fingerprint

Dive into the research topics of 'CellTypeGraph: A New Geometric Computer Vision Benchmark'. Together they form a unique fingerprint.

Cite this