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