biostudies-literatureUnknownGoldgof GMNHLBI NIH HHSMorphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating rbioRxiv : the preprint server for biology2023.02.20.528987https://www.ebi.ac.uk/biostudies/studies/S-EPMC9979993biostudies-literatureDeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performance.PMC9979993K38 HL159128R38 HL143581Dogan ABaik JLu CMGoldgof DBGoldgof GMWang LZhu MVan Cleave JLucas FArdon OBrown LPrakash SSun SSpector JDCarmichael IBoiocchi LButte AJfalseDeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performance.Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating r2023-01-01T00:00:00Z2023 Feb2025-04-05T11:27:36.005Z2025-04-05T11:27:36.005ZS-EPMC99799933686521610.1101/2023.02.20.528987