Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology
This addresses the need for more accurate and efficient diagnostic workflows in hematopathology, potentially revolutionizing clinical decision-making, though it is an incremental advance in applying deep learning to a specific medical domain.
The researchers tackled the problem of automating bone marrow cytology, which is tedious and prone to variability, by developing an end-to-end deep learning system that detects regions and classifies cells with high accuracy, achieving metrics such as 0.97 accuracy in region detection and 0.75 mAP in cell classification.
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.