Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset.

Journal: Artificial intelligence in medicine
PMID: 26303104

Abstract

OBJECTIVE: This paper presents benchmarking results of human epithelial type 2 (HEp-2) interphase cell image classification methods on a very large dataset. The indirect immunofluorescence method applied on HEp-2 cells has been the gold standard to identify connective tissue diseases such as systemic lupus erythematosus and Sjögren's syndrome. However, the method suffers from numerous issues such as being subjective, time consuming and labor intensive. This has been the main motivation for the development of various computer-aided diagnosis systems whose main task is to automatically classify a given cell image into one of the predefined classes.

Authors

  • Peter Hobson
    Sullivan Nicolaides Pathology, 134 Whitmore street, Taringa, Queensland 4068, Australia. Electronic address: peter_hobson@snp.com.au.
  • Brian C Lovell
    School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia. Electronic address: lovell@itee.uq.edu.au.
  • Gennaro Percannella
    Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, SA I-84084, Italy. Electronic address: pergen@unisa.it.
  • Mario Vento
    Dept. of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Italy.
  • Arnold Wiliem
    School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia. Electronic address: a.wiliem@uq.edu.au.

Keywords

External Resources

Popular Topics
Recent Journals