Jiang, Y.; Shen, Y.; Wang, Y.; Ding, Q. Automatic Recognition of White Blood Cell Images with Memory Efficient Superpixel Metric GNN: SMGNN. Mathematical Biosciences and Engineering 2024, 21, 2163–2188, doi:10.3934/mbe.2024095.
Jiang, Y.; Shen, Y.; Wang, Y.; Ding, Q. Automatic Recognition of White Blood Cell Images with Memory Efficient Superpixel Metric GNN: SMGNN. Mathematical Biosciences and Engineering 2024, 21, 2163–2188, doi:10.3934/mbe.2024095.
Jiang, Y.; Shen, Y.; Wang, Y.; Ding, Q. Automatic Recognition of White Blood Cell Images with Memory Efficient Superpixel Metric GNN: SMGNN. Mathematical Biosciences and Engineering 2024, 21, 2163–2188, doi:10.3934/mbe.2024095.
Jiang, Y.; Shen, Y.; Wang, Y.; Ding, Q. Automatic Recognition of White Blood Cell Images with Memory Efficient Superpixel Metric GNN: SMGNN. Mathematical Biosciences and Engineering 2024, 21, 2163–2188, doi:10.3934/mbe.2024095.
Abstract
An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based system. In this paper, we present a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consume substantial computational resources and increase the risk of overfitting, especially when limited medical image samples are available for training.
To address these challenges, we propose a novel memory-efficient strategy that exploits graph structures derived from the images.
Specifically, we introduce a lightweight superpixel-based Graph Neural Network (GNN) and break new ground by introducing superpixel metric learning to segment nucles and cytoplasm. Remarkably, our proposed segmentation model (SMGNN) achieves state-of-the-art segmentation performance while utilizing at most 10000$\times$ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes show satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories.
Keywords
Graph Neural Networks; Superpixel metric learning; Memory efficient model; White blood cell segmentation; Cell type classification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
