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.

FCM-Former 是一个自动化儿童急性白血病免疫表型评估的机器学习、自注意力基础的流式细胞分析工具，准确度达 96.5%，利用流式细胞数据为每个样本分配细胞系，是首个使用 FCM 数据自动化免疫表型评估儿科急性白血病的工作。