2 papers
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…
q-bio.QM2023
Explainable Techniques for Analyzing Flow Cytometry Cell Transformers
Florian Kowarsch, Lisa Weijler, FLorian Kleber +4
Explainability for Deep Learning Models is especially important for clinical applications, where decisions of automated systems have far-reaching consequences. While various post-h…