Active microscopic cellular image annotation by superposable graph transduction with imbalanced labels

Jun Wang, Shih Fu Chang, Xiaobo Zhou, Stephen T.C. Wong

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

36 Scopus citations

Abstract

Systematic content screening of cell phenotypes in microscopic images has been shown promising in gene function understanding and drug design. However, manual annotation of cells and images in genome-wide studies is cost prohibitive. In this paper, we propose a highly efficient active annotation framework, in which a small amount of expert input is leveraged to rapidly and effectively infer the labels over the remaining unlabeled data. We formulate this as a graph based transductive learning problem and develop a novel method for label propagation. Specifically, a label regularizer method is proposed to handle the important label imbalance issue, typically seen in the cellular image screening applications. We also design a new scheme which breaks the graph into linear superposition of contributions from individual labeled samples. We take advantage of such a superposable representation to achieve fast annotation in an interactive setting. Extensive evaluations over toy data and realistic cellular images confirm the superiority of the proposed method over existing alternatives.

Original languageEnglish (US)
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
CountryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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