2. first-timer 3. label effort1. many types
?Construct cell size distribution
Learn fromprevious types
label effortrandom
Select most importantsamples for user to label.
User
Training ImageCell
Training Samples
Non-cell
GATLAB
Size Distribution
Previous typesinteractive
Training ImageCell
Training Samples
Non-cell
GATLAB
Size Distribution
Detection Confidence
User
Previous types
AdaBoost uses Adaptive Boosting
TaskTrAdaBoost learns from previous cell types
GlobalTrAdaBoost obtains cell size distribution
GATLAB selects most important samples
Nguyen et al. (2011)
Yao and Doretto (2010)
Freund and Schapire (2000)
An accurate cell detection algorithm.
Require minimal training effort.
Help biologists to study various cell types.
N. Nguyen, E. Norris, M. Clemens, M. Shin. “Rapidly Adaptive Cell Detection.” Machine Vision and Applications (MVA), Special Issue: Machine Learning in Medical Imaging [in review].
N. Nguyen and M. Shin. “Active Transfer Boosting to Reduce Training Effort in Multi-class Data classification." IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, June 18-20, 2012 [in review].
N. Nguyen, E. Norris, M. Clemens, M. Shin. “Rapidly Adaptive Cell Detection using Transfer Learning with a Global Parameter.” The Second International Workshop on Machine Learning in Medical Imaging (MLMI), Toronto, Canada. September 18-22, 2011.
N. Nguyen, S. Keller, E. Norris, T. Huynh, M. Clemens, M. Shin. “Tracking Colliding Cells in vivo Microscopy Video.” IEEE Transactions on Biomedical Engineering (TBE), 58(8):2391-2400, August 2011.
N. Nguyen, S. Keller, T. Huynh, M. Shin. “Tracking Colliding Cells”. IEEE Workshop on Applications of Computer Vision (WACV), Snowbird, UT December 07-09, 2009.