most citedOn the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data

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eess.IV2023

FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data

Lisa Weijler, Florian Kowarsch, Michael Reiter +3

While model architectures and training strategies have become more generic and flexible with respect to different data modalities over the past years, a persistent limitation lies…