
This paper introduces FlowCyt, a benchmark for multi-class single-cell classification in flow cytometry data. The authors showcase the effectiveness of Graph Neural Networks (GNNs) in exploiting spatial relationships for superior performance.


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FlowCyt-A-Comparative-Study-of-Deep-Learning-Approaches-for-Multi-Class-Classification-in-Flow-Cytometry-Benchmarking


FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking


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FlowCyt presents a benchmark for multi-class single-cell classification in flow cytometry data, showcasing the effectiveness of Graph Neural Networks (GNNs) in exploiting spatial relationships.


flowcyt-a-comparative-study-of-deep-learning-approaches-for-multi-class-classification-in-flow-cytometry-benchmarking


FlowCyt presents a benchmark for multi-class single-cell classification in flow cytometry data, emphasizing the importance of graph neural networks for superior performance.
