Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells

  • Yongsheng Pan
  • Mingxia Liu
  • Yong XiaEmail author
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


Classification of normal and malignant cells observed under a microscope is an essential and challenging step in the development of a cost-effective computer-aided diagnosis tool for acute lymphoblastic leukemia. In this paper, we propose the neighborhood-correction algorithm (NCA) to address this challenge, which consists of three major steps, including (1) fine-tuning a pretrained residual network using training data and producing initial labels and feature maps for test data, (2) constructing a Fisher vector for each cell image based on its feature maps, and (3) correcting the initial label of each test cell image via the weighted majority voting based on its most similar neighbors. We have evaluated this algorithm on the database provided by the grand challenge on the classification of normal and malignant cells (C-NMC) in B-ALL white blood cancer microscopic images. Experimental results demonstrate that our proposed NCA achieves the weighted F1-score of 92.50% and balanced accuracy of 91.73% in the preliminary testing and achieves weighted F1-score of 91.04% in the final testing, which ranks the first in C-NMC. Associated code is available at


Microscopic image classification Fisher vector Residual network Leukemia B-lymphoblast cells 



Y. Pan and Y. Xia were supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grants 61771397, in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grants CX201835, and in part by the Project for Graduate Innovation team of Northwestern Polytechnical University.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Research & Development Institute of Northwestern Polytechnical UniversityShenzhenChina
  2. 2.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA

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