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Hybrid adversarial-discriminative network for leukocyte classification in leukemia.

Authors
  • Zhang, Chuanhao1
  • Wu, Shangshang1
  • Lu, Zhiming2
  • Shen, Yajuan2
  • Wang, Jing2
  • Huang, Pu1
  • Lou, Jingjiao1
  • Liu, Cong1
  • Xing, Lei3
  • Zhang, Jian2
  • Xue, Jie4
  • Li, Dengwang1
  • 1 Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China. , (China)
  • 2 Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong University, 250014, China. , (China)
  • 3 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 94304, USA.
  • 4 Business School, Shandong Normal University, 250014, China. , (China)
Type
Published Article
Journal
Medical Physics
Publisher
Wiley (John Wiley & Sons)
Publication Date
Aug 01, 2020
Volume
47
Issue
8
Pages
3732–3744
Identifiers
DOI: 10.1002/mp.14144
PMID: 32180243
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis. © 2020 American Association of Physicists in Medicine.

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