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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.18309 |
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| _version_ | 1866916302008877056 |
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| author | Lygizou, Elpiniki Maria Reiter, Michael Maurer-Granofszky, Margarita Dworzak, Michael Grosu, Radu |
| author_facet | Lygizou, Elpiniki Maria Reiter, Michael Maurer-Granofszky, Margarita Dworzak, Michael Grosu, Radu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers Lygizou, Elpiniki Maria Reiter, Michael Maurer-Granofszky, Margarita Dworzak, Michael Grosu, Radu Machine Learning Quantitative Methods 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. |
| title | Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2406.18309 |