Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
Abstrak
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 Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median F1 score of ~0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets
Penulis (11)
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 Dworzak
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓