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Found 2 papers, 1 papers with code
FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data
This limitation becomes particularly relevant in scenarios where the attributes captured during data acquisition vary across different samples.
Explainable Techniques for Analyzing Flow Cytometry Cell Transformers
Explainability for Deep Learning Models is especially important for clinical applications, where decisions of automated systems have far-reaching consequences.
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