  Bone marrow cytology is required to make a hematological diagnosis,
influencing critical clinical decision points in hematology. However, bone
marrow cytology is tedious, limited to experienced reference centers and
associated with high inter-observer variability. This may lead to a delayed or
incorrect diagnosis, leaving an unmet need for innovative supporting
technologies. We have developed the first ever end-to-end deep learning-based
technology for automated bone marrow cytology. Starting with a bone marrow
aspirate digital whole slide image, our technology rapidly and automatically
detects suitable regions for cytology, and subsequently identifies and
classifies all bone marrow cells in each region. This collective
cytomorphological information is captured in a novel representation called
Histogram of Cell Types (HCT) quantifying bone marrow cell class probability
distribution and acting as a cytological "patient fingerprint". The approach
achieves 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 revolutionize
hematopathology diagnostic workflows, leading to more cost-effective, accurate
diagnosis and opening the door to precision medicine.
