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Comparison of anomaly detection and supervised learning for acute myeloid leukemia blast identification: A proof-of-concept study
Background. Flow cytometry is the established standard for identifying leukemic blast populations in AML, but relies predominantly on subjective, time-consuming manual gating. Machine learning approaches are being explored to support automated blast detection, yet the relative performance of anomaly
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