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Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification
- Source :
- EMBC
- Publication Year :
- 2020
-
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.
- Subjects :
- Oncology
medicine.medical_specialty
Manual interpretation
Neoplasm, Residual
0206 medical engineering
02 engineering and technology
Automation
Deep Learning
hemic and lymphatic diseases
Internal medicine
0502 economics and business
medicine
Humans
Retrospective Studies
business.industry
05 social sciences
Myeloid leukemia
Flow Cytometry
020601 biomedical engineering
Autoencoder
Phenotype
Minimal residual disease
Large cohort
Clinical Practice
Leukemia, Myeloid, Acute
Hematological Diseases
Area Under Curve
Hematologic Neoplasms
050211 marketing
business
Subjects
Details
- ISSN :
- 26940604
- Volume :
- 2019
- Database :
- OpenAIRE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Accession number :
- edsair.doi.dedup.....dc4915dccf79f44bcbfd99bec52122d8