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Bone marrow cytology is required to make a hematological diagnosis,influencing critical clinical decision points in hematology. However, bonemarrow cytology is tedious, limited to experienced reference centers andassociated with high inter-observer variability. This may lead to a delayed orincorrect diagnosis, leaving an unmet need for innovative supportingtechnologies. We have developed the first ever end-to-end deep learning-basedtechnology for automated bone marrow cytology. Starting with a bone marrowaspirate digital whole slide image, our technology rapidly and automaticallydetects suitable regions for cytology, and subsequently identifies andclassifies all bone marrow cells in each region. This collectivecytomorphological information is captured in a novel representation calledHistogram of Cell Types (HCT) quantifying bone marrow cell class probabilitydistribution and acting as a cytological "patient fingerprint". The approachachieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC),and cell detection and cell classification (0.75 mAP, 0.78 F1-score,Log-average miss rate of 0.31). HCT has potential to revolutionizehematopathology diagnostic workflows, leading to more cost-effective, accuratediagnosis and opening the door to precision medicine.

Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology