Detecting Rare Cell Populations in Flow Cytometry Data Using UMAP. - Lisa Weijler; Markus Diem; Michael Reiter; Margarita Maurer-Granofszky (ICPR, 2020)

Automated Identification of Cell Populations in Flow Cytometry Data with Transformers. - Matthias Wödlinger; Michael Reiter; Lisa Weijler; Margarita Maurer-Granofszky; Angela Schumich; Michael N. Dworzak (CoRR, 2021)

Towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data. - Florian Kowarsch; Lisa Weijler; Matthias Wödlinger; Michael Reiter; Margarita Maurer-Granofszky; Angela Schumich; Elisa O. Sajaroff; Stefanie Groeneveld-Krentz; Jorge G. Rossi; Leonid Karawajew; Richard Ratei; Michael N. Dworzak (iMIMIC@MICCAI, 2022)

Automated identification of cell populations in flow cytometry data with transformers. - Matthias Wödlinger; Michael Reiter; Lisa Weijler; Margarita Maurer-Granofszky; Angela Schumich; Elisa O. Sajaroff; Stefanie Groeneveld-Krentz; Jorge G. Rossi; Leonid Karawajew; Richard Ratei; Michael N. Dworzak (Comput. Biol. Medicine, 2022)

Explainable Techniques for Analyzing Flow Cytometry Cell Transformers. - Florian Kowarsch; Lisa Weijler; Florian Kleber; Matthias Wödlinger; Michael Reiter; Margarita Maurer-Granofszky; Michael N. Dworzak (CoRR, 2023)

Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers. - Elpiniki Maria Lygizou; Michael Reiter; Margarita Maurer-Granofszky; Michael N. Dworzak; Radu Grosu (EMBC, 2024)

On the Importance of Local and Global Feature Learning for Automated Measurable Residual Disease Detection in Flow Cytometry Data. - Lisa Weijler; Michael Reiter; Pedro Hermosilla; Margarita Maurer-Granofszky; Michael N. Dworzak (ICPR (12), 2024)

FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data. - Lisa Weijler; Florian Kowarsch; Michael Reiter; Pedro Hermosilla; Margarita Maurer-Granofszky; Michael N. Dworzak (WACV, 2024)