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On the Classification of a Small Imbalance Cytogenetic Image Database

This study presents solutions to enhance accuracy in classifying small, imbalanced cytogenetic images. The research focuses on nucleus and signal segmentation, hierarchical strategies, and signal classification using techniques such as Naïve Bayesian Classifier (NBC), Gaussian estimation, Gaussian mixture model, and Multilayer Perceptron Neural Network. Results show significant improvements in accuracy and the detection of non-dot-like to dot-like FISH signals. The paper also emphasizes data balancing and dimensionality reduction to tackle the challenges posed by small sample sizes and high-dimensional patterns.

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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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