An Accurate Cell Detection with Minimal Training Effort

healthy

stress, viral infection, drug intake

Leukemia (blood cancer)

flu, poisoning

What would aid them to move toward cancer tumor?

Incorrectly detected cell

Undetectedcell

Correctly detected cell

AutomaticDetection

100

clicks

AutomaticDetection

10

clicks

Incorrectly detected cell

Undetectedcell

Correctly detected cell

OurMethod

10

clicks

Incorrectly detected cell

Undetectedcell

Correctly detected cell

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

Red Blood CellsDrosophilaNatural Killer THT29 CancerWhite Blood Cells

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)

Training samples were selected from 1 to 10.Execute training and testing 30 times.

Training samples were selected up to 100 samples.

Natural Killer T-cells

AdaBoost

GATLAB

HT29 Colon Cancer

AdaBoost

GATLAB

*presented in the 11th GRF (2011)

Natural Killer T-cells

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.

Min Shin, PhD Mark Clemens, PhD Eric Norris, MS Toan Huynh, MD Steve Keller, MS