Core Concepts
FlowCyt presents a benchmark for multi-class single-cell classification in flow cytometry data, emphasizing the importance of graph neural networks for superior performance.
Stats
Experiments utilize supervised inductive learning and semi-supervised transductive learning on up to 1 million cells per patient.
GNNs demonstrate superior performance by exploiting spatial relationships in graph-encoded data.
The dataset comprises bone marrow samples from 30 patients, with each cell characterized by twelve markers.
Quotes
"GNNs demonstrate superior performance by exploiting spatial relationships in graph-encoded data."
"The benchmark allows standardized evaluation of clinically relevant classification tasks."