An Accurate Cell Detection with Minimal Training Effort
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Wining talk at the 12th Annual Graduate Research Fair at UNC Charlotte. This is the practice version.
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An Accurate Cell Detection with Minimal Training Effort
- 3. Leukemia (blood cancer) stress, viral infection, drug intake healthy flu, poisoning
- 4. What would aid them to move toward cancer tumor?
- 6. Automatic Detection 100 clicks Incorrectly Undetected Correctly detected cell cell detected cell
- 7. Automatic Detection 10 clicks Incorrectly Undetected Correctly detected cell cell detected cell
- 8. Our Method 10 clicks Incorrectly Undetected Correctly detected cell cell detected cell
- 10. 1. many types 2. first-timer 3. label effort Construct cell Learn from size distribution previous types ?
- 11. Training Image Cell Non- cell Training Samples User Size Distribution GATLAB label effort random interactive Previous types Select most important samples for user to label.
- 12. Training Image Cell Non- cell Training Samples User Size Distribution Detection Confidence GATLAB Previous types
- 14. White Blood Cells HT29 Cancer Natural Killer T Drosophila Red Blood Cells
- 15. AdaBoost uses Adaptive Boosting TaskTrAdaBoost learns from previous cell types GlobalTrAdaBoost obtains cell size distribution GATLAB selects most important samples Freund and Schapire (2000) Yao and Doretto (2010) Nguyen et al. (2011)
- 16. Training samples were selected from 1 to 10. Execute training and testing 30 times.
- 17. Training samples were selected up to 100 samples.
- 19. AdaBoost
- 20. GATLAB
- 22. AdaBoost
- 23. GATLAB
- 24. Natural Killer T-cells *presented in the 11th GRF (2011)
- 25. An accurate cell detection algorithm. Require minimal training effort. Help biologists to study various cell types.
- 26. 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.
- 27. Min Shin, PhD Mark Clemens, PhD Eric Norris, MS Toan Huynh, MD Steve Keller, MS
Notes de l'éditeur
- A special type of white blood cells, call natural killer t-cells, has a potential of killing cancer tumor.
- 10 training samples, which is only 10% of the training effort as
- Our previous research has solved the first 2 of these challenges.
- Elaborate much more in this one.
- Elaborate much more in this one.
- Need to have all four methods. 1 figure that said it all. Zoom in on the 1 to 10 number of training samples.
- Need to have all four methods. 1 figure that said it all. Zoom in on the 1 to 10 number of training samples.
- And finally, thank you for listening.