TY - GEN
T1 - Active microscopic cellular image annotation by superposable graph transduction with imbalanced labels
AU - Wang, Jun
AU - Chang, Shih Fu
AU - Zhou, Xiaobo
AU - Wong, Stephen T.C.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=51949094765&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51949094765&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2008.4587746
DO - 10.1109/CVPR.2008.4587746
M3 - Conference contribution
AN - SCOPUS:51949094765
SN - 9781424422432
T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Y2 - 23 June 2008 through 28 June 2008
ER -