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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis.

Shenghuan Sun¹, Zhanghan Yin^2,3,4, Jacob G Van Cleave^2,3

¹Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA.

Science Translational Medicine

|June 11, 2025

Summary

DeepHeme, a deep learning model, accurately classifies bone marrow cells, matching or exceeding human expert performance. This AI tool enhances diagnostic efficiency for hematological disorders.

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Area of Science:

Computational pathology
Artificial intelligence in hematology
Medical diagnostics

Background:

Bone marrow aspirate (BMA) cytomorphology is crucial for diagnosing hematological disorders but is complex and error-prone.
Current deep learning models for BMA analysis lack expert-level accuracy and generalizability.

Purpose of the Study:

To develop and validate a deep learning model for accurate and generalizable bone marrow cell classification.
To achieve expert-level performance in automated hematopathology slide analysis.

Main Methods:

Developed DeepHeme, a snapshot ensemble deep learning classifier using a curated dataset of 30,394 bone marrow images.
Trained and tested DeepHeme on data from the University of California, San Francisco, and validated on an independent dataset from Memorial Sloan Kettering Cancer Center.
Compared DeepHeme's cell classification performance against three human hematopathology experts.

Main Results:

DeepHeme achieved higher accuracy than previous models and classified more cell types.
External validation demonstrated robust generalizability across different datasets and WSI systems.
DeepHeme's diagnostic performance was comparable to or exceeded that of human experts in individual cell classifications.

Conclusions:

DeepHeme represents a significant advancement in automated hematopathology slide analysis.
Accurate and generalizable AI-driven cell classification facilitates the development of predictive markers.
This technology has the potential to improve diagnostic efficiency and accuracy in hematological disorder workup.