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Updated: Jul 3, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Edge-prior and reliability-guided collaborative learning for white blood cell classification.

Rong Gao¹, Qi Ke¹, Aiquan Li¹

¹Guangxi University of Finance and Economics, Nanning, 530003, China.

Scientific Reports

|July 1, 2026

Summary

This summary is machine-generated.

This study introduces a novel framework for accurate white blood cell (WBC) classification, improving automated analysis of blood smear images. The method effectively combines local cell details with broader context for enhanced diagnostic accuracy.

Keywords:

Dual-stream collaborative learning Reliability-guided bilateral fusion Structure-aided attention fusion Visual state space model White blood cell classification

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Area of Science:

Hematology
Computer Vision
Medical Image Analysis

Background:

Accurate white blood cell (WBC) classification is crucial for hematological screening and computer-aided diagnosis.
Manual microscopic examination is labor-intensive and prone to observer variability, especially for similar WBC subtypes.
Existing deep learning models struggle with fine-grained classification requiring integration of local morphology and global context.

Purpose of the Study:

To develop an advanced deep learning framework for accurate and automated white blood cell (WBC) classification.
To enhance the coordination between local morphological features and global cellular context in WBC image analysis.
To improve the reliability and robustness of automated WBC subtype identification.

Main Methods:

A collaborative framework integrating a ConvNeXt branch for local morphology and a Mamba-based branch for long-range context.
Implementation of a Structure-Aided Attention Fusion module utilizing multi-scale edge priors for feature alignment.
Employment of reliability-guided bilateral fusion based on predictive entropy, maximum class probability, and classification margin.

Main Results:

Achieved high image-level classification accuracies of 99.32% (PBC), 98.18% (LDWBC), and 99.03% (Raabin-WBC) datasets.
Demonstrated the framework's effectiveness through ablation studies, transfer learning, robustness, and interpretability tests.
Confirmed the model's stability and performance on public benchmarks for WBC classification.

Conclusions:

The proposed edge-prior and reliability-guided collaborative framework significantly advances automated white blood cell (WBC) classification.
The integration of local and global features with attention mechanisms offers a robust solution for fine-grained subtype identification.
The framework shows strong feasibility for real-world applications in hematological analysis and computer-aided diagnostics.