Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers
| Autor: | Lygizou, Elpiniki Maria, Reiter, Michael, Maurer-Granofszky, Margarita, Dworzak, Michael, Grosu, Radu |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: The paper has been accepted at IEEE EMBS 2024 (46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society) |
| Databáze: | arXiv |
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