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

TL;DR

This paper introduces an innovative deep learning system for automated bone marrow cytology, capable of detecting regions, classifying cells, and creating a novel patient fingerprint, thereby improving diagnostic accuracy and efficiency.

Contribution

It presents the first end-to-end deep learning approach for bone marrow cytology, including a new Histogram of Cell Types representation for patient profiling.

Findings

High accuracy in region detection (0.97 accuracy, 0.99 ROC AUC)

Effective cell detection and classification (0.75 mAP, 0.78 F1-score)

Potential to enhance diagnostic workflows in hematopathology

Abstract

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.