Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers
<|reference_start|>Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers: 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 on Multiparameter Flow Cytometry (FCM). However, this approach is manual, and thus time-consuming and subjective. To alleviate this situation, we propose in this paper the FCM-Former, a machine learning, self-attention based FCM-diagnostic tool, automating the immunophenotyping assessment in Childhood Acute Leukemia. The FCM-Former is trained in a supervised manner, by directly using flow cytometric data. Our FCM-Former achieves an accuracy of 96.5% assigning lineage to each sample among 960 cases of either acute B-cell, T-cell lymphoblastic, and acute myeloid leukemia (B-ALL, T-ALL, AML). To the best of our knowledge, the FCM-Former is the first work that automates the immunophenotyping assessment with FCM data in diagnosing pediatric Acute Leukemia.<|reference_end|>
@article{lygizou2024automated,
    title={Automated Immunophenotyping Assessment for Diagnosing Childhood Acute
  Leukemia using Set-Transformers},
    author={Elpiniki Maria Lygizou, Michael Reiter, Margarita Maurer-Granofszky,
  Michael Dworzak, Radu Grosu},
    journal={arXiv preprint arXiv:2406.18309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18309},
    primaryClass={cs.LG q-bio.QM}
}