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Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification
Abstract
Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients’ FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.
Figures
The overall framework to learn deep cytometric phenotype embedding for AML and MDS classification.
The overall framework to learn deep cytometric phenotype embedding for AML and MDS classification.
Results of tube selection are shown. The x-axis indicates number of tubes included with respect to the rank listed in Table IV.
Results of tube selection are shown. The x-axis indicates number of tubes included with respect to the rank listed in Table IV.
Publication Date
2019/07/23
Conference
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI
10.1109/embc.2019.8856728
Publisher
IEEE