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On the Classification of a Small Imbalance Cytogenetic Image Database

On the Classification of a Small Imbalance Cytogenetic Image Database. Presenter : Ai-Chen Liao Authors : Boaz Lerner, Josepha Yeshaya, and Lev Koushnir. 2007 . TCBB . Page : 204 - 215. Outline. Motivation Objective Method Experimental Result Conclusion

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On the Classification of a Small Imbalance Cytogenetic Image Database

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  1. On the Classification of a Small Imbalance Cytogenetic Image Database Presenter : Ai-Chen Liao Authors : Boaz Lerner, Josepha Yeshaya, and Lev Koushnir 2007 . TCBB . Page : 204 - 215

  2. Outline • Motivation • Objective • Method • Experimental Result • Conclusion • Comments

  3. Motivation • Small sample size, large number of features, and the complexity of the classification rule, may also deteriorate classifier accuracy. • Solving a multiclass classification task using a small imbalanced database of patterns of high dimension is difficult due to the curse-of-dimensionality and the bias of the training toward the majority classes. FISH：（螢光定點染色 OR 螢光原位雜交法） 利用螢光，標定DNA探針，藉由雜交的過程，在染色體上將DNA或基因定位。

  4. Objective • We propose and experimentally study using the cytogenetic domain two solutions to the problem and contributed to accuracy improvement.

  5. Method

  6. Method

  7. Method ─ Nucleus and Signal Segmentation 7

  8. Method 8

  9. Method ─ Hierarchical Strategy {All signals} : 367 {R1,R2,D} : 193 {S,N} : 174 {R2,D} : 87 {R1} : 106 {N} : 56 {S} : 118 {D} : 44 {R2} : 43 9

  10. Method 10

  11. Method ─ Signal Classification • The Naïve Bayesian Classifier (NBC) • Single Gaussian Estimation • Kernel Density Estimation • A Gaussian Mixture Model • Multilayer Perceptron Neural Network (MLP) 11

  12. Method ─ NBC • The Naïve Bayesian Classifier (NBC) • Single Gaussian Estimation • Kernel Density Estimation • A Gaussian Mixture Model 12

  13. Method ─ MLP 13

  14. Experimental Results

  15. Experimental Results High NBC-KDE MLP NBC-SGE NBC-GMM

  16. Experimental Results

  17. Conclusion • The first contribution of the paper is in the automatic classification of a small, imbalanced cytogenetic image database. • Hierarchical task decomposition • Balancing the data together with dimensionality reduction • The second contribution is in detecting and classifying non-dot-like together with dot-like FISH signals, as previous study concentrated on dot-like signals only.

  18. Comments • Advantage • A novel process • Drawback • It’s writing way is too hard to understand. • Application • Handling imbalanced data

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