Multi-layer Model Collaboration for Bioimage Temporal Stage Classification

Tao Meng, Mei Ling Shyu

Research output: Contribution to journalArticlepeer-review


Nowadays, bioimages such as microscopic images and in situ hybridization images increase exponentially. The rapid growth of such images calls for efficient and effective methods for mining significant patterns in them. As a biological process usually consists of several temporal stages, one important task in bioimage analysis is to classify images into different stages. In this paper, a multi-layer model collaboration approach is proposed to capitalize the class correlations in order to enhance the multi-class classification accuracy. First, several middle-level classes, which are relatively easy to annotate are created. A set of subspace-based classifiers are trained. Next, the classification scores output from these models are integrated with the target class classification scores. The score integration problem was formulated as a convex optimization problem, which is solved by the gradient descent approach. Experiments on four biological image data sets demonstrate that the proposed framework outperforms other current state-of-the-art algorithms, which indicates the proposed framework is promising.

Original languageEnglish (US)
Pages (from-to)123-144
Number of pages22
JournalInternational Journal of Semantic Computing
Issue number2
StatePublished - Jun 1 2014


  • Bioimage data mining
  • multilayer model collaboration
  • temporal stage annotation

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Linguistics and Language
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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