30 citations · 31 across the 5 of their papers we have counts for
5 papers
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 +2
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of mode…
Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers
Elpiniki Maria Lygizou, Michael Reiter, Margarita Maurer-Granofszky +2
Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based o…
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…
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…
Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
Matthias Wödlinger, Michael Reiter, Lisa Weijler +8
Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disea…