Histology image classification using supervised classification and multimodal fusion

Tao Meng, Lin Lin, Mei Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
Pages145-152
Number of pages8
DOIs
StatePublished - Dec 1 2010
Event2010 IEEE International Symposium on Multimedia, ISM 2010 - Taichung, Taiwan, Province of China
Duration: Dec 13 2010Dec 15 2010

Publication series

NameProceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010

Other

Other2010 IEEE International Symposium on Multimedia, ISM 2010
CountryTaiwan, Province of China
CityTaichung
Period12/13/1012/15/10

Keywords

  • C-RSPM
  • Histology image classification
  • Multimodal fusion
  • Weighted majority voting algorithm

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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  • Cite this

    Meng, T., Lin, L., Shyu, M. L., & Chen, S. C. (2010). Histology image classification using supervised classification and multimodal fusion. In Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010 (pp. 145-152). [5693834] (Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010). https://doi.org/10.1109/ISM.2010.29