Multi-streams and multi-features for cell classification

Xinpeng Xie, Yuexiang Li, Menglu Zhang, Yong Wu, Linlin Shen

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

7 Citations (Scopus)

Abstract

With the development of deep learning technique, cell classification has gained increasing interests from the community. Identifying malignant cells in B-ALL white blood cancer microscopic images is challenging, since the normal and malignant cells have similar appearances. Traditional cell identification approach requires experienced pathologists to carefully read the cell images, which is laborious and suffers from inter-observer variations. Hence, the computer aid diagnosis systems for blood disorders, for example, leukemia, are worthwhile to develop. In this paper, we design a multi-stream model to classify the immature leukemic blasts and normal cells. We evaluated the proposed model on the C-NMC 2019 challenge dataset. The experimental results show that a promising result is achieved by our model.

Original languageEnglish
Title of host publicationLecture Notes in Bioengineering
PublisherSpringer
Pages95-102
Number of pages8
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameLecture Notes in Bioengineering
ISSN (Print)2195-271X
ISSN (Electronic)2195-2728

Keywords

  • Deep learning network
  • Feature fusion
  • Leukemia cell identification

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Biomedical Engineering

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