1. Integration of Radiologists Feedback into Computer-Aided Diagnosis Systems Sarah A. Jabon a Daniela S. Raicu b Jacob D. Furst b a Rose-Hulman Institute of Technology, Terre Haute, IN 47803 b School of Computing, CDM, DePaul Universtiy, Chicago, IL 60604

2. Overview Introduction Related Work The Data Methodology Simple Distance Metrics Linear Regression Principle Component Analysis Results Simple Distance Metrics Linear Regression Principle Component Analysis Conclusions Future Work

3. Introduction The 2008 official estimate 215,020 cases diagnosed 161,840 deaths will occur Five-year relative-survival rate (1996 – 2004): 15.2% Computer-aided diagnosis systems can help improve early detection

4. Related Work El-Naqa et al. mammography images neural networks and support vector machines Muramatsu et al. mammography images. three-layered artificial neural network to predict the semantic similarity rating between two nodules Park et al. linear distance-weighted K-nearest neighbor algorithm to identify similar images

5. Related Work ASSERT by Purdue University Content-based features: co-occurrence, shape, Fourier Transforms, global gray level statistics Radiologists also provide features BiasMap by Zhou and Huang Relevance feedback, content-based features Analysis: biased-discriminant analysis (BDA)

6. The Data Lung Image Database Consortium Reduced 1,989 images down to 149 (one for each nodule) Summarized the radiologists ratings (up to 4) into a single vector Each nodule has 7 semantic based characteristics and 64 content-based characteristics

7. Overview Introduction Related Work The Data Methodology Simple Distance Metrics Linear Regression Principle Component Analysis Results Simple Distance Metrics Linear Regression Principle Component Analysis Conclusions Future Work

8. Methodology

9. Methodology: Simple Distance Metrics Semantic-Based Similarity Content-Based Similarity

10. Simple Distance Metrics Content-Based Similarity Values (Euclidean) Semantic-Based Similarity Values (1 – Cosine)

11. Methodology: Linear Regression

12. Methodology: Principle Component Analysis LobulationMalignancyMarginSphericitySpiculationSubtletyTexture Lobulation 1.000.199.085-.008.815.065.101 Malignancy.1991.000.346.187.155.594.351 Margin.085.3461.000.391.109.533.717 Sphericity -.008.187.3911.000.078.300.230 Spiculation.815.155.109.0781.000.156.146 Subtlety.065.594.533.300.1561.000.523 Texture.101.351.717.230.146.5231.000 Content-Based Features: 77 pairs with a correlation > 0.9 136 pairs with a correlation > 0.8 or < -0.8

13. Scree Plots: 5 – 9 Matches

14. Methodology: Principle Component Analysis PCA on content-based features accounts for 99% of the variance 23 components PCA on semantic-based characteristics Method 1 accounts for 92% of the variance 4 components Method 2 accounts for 98% of the variance 6 components

15. Overview Introduction Related Work The Data Methodology Simple Distance Metrics Linear Regression Principle Component Analysis Results Simple Distance Metrics Linear Regression Principle Component Analysis Conclusions Future Work

16. Results: Simple Distance Metric MatchesGaborMarkov Co- Occurrence Gabor, Markov, and Co-Occurrence All Features 6 – 102418313643 2 – 5107104949893 0 – 11827241513

17. Matches: Nodule 117

18. Simple Distance Metrics

19. 5 – 9 Matches: PCA and Linear Regression Linear Regression Principle Component Analysis Training and Testing Sets 5 – 9 Matches 326 Nodule Pairs 218 Nodule Pairs 218 Nodule Pairs PCAd Linear RegressionPCA 108 Nodule Pairs 108 Nodule Pairs PCAd Predicted Similarity Value

20. Results: Linear Regression Data Set No. of Nodule Pairs ( 2/3 Set) Correlation: Euclidean vs. Semantic R2R2 Adj. R 2 Feature Set Distance 6 – 9 Matches 166-0.0160.9480.8712- 6 – 9 Matches 166-0.0160.8020.6791dist 3 5 – 9 Matches 218-0.0060.9270.8502- 5 – 9 Matches 218-0.0060.7330.6241dist 3

21. Results: Linear Regression Data Set No. of Nodule Pairs (1/3 Set) Correlation: Euclidean vs. Semantic RMSD Euclidean Correlation: Predicted vs. Semantic RMSD Predicted Features 6 – 9 Matches 85-0.0230.23280.7100.0242128 6 – 9 Matches 85-0.0230.23280.7480.018164 5 – 9 Matches 108-0.0390.19850.8290.0136128 5 – 9 Matches 108-0.0390.19850.7330.015564

22. Results: Linear Regression

24. Results: PCA Data Set No. of Nodule Pairs ( 1/3 Set) Correlation: Euclidean vs. Semantic RMSD Euclidean Correlation: Predicted vs. Semantic RMSD Predicted Features 6 – 9 Matches 85-0.1150.30430.7870.0061128 6 – 9 Matches 85-0.1150.30430.3930.011464 5 – 9 Matches 108-0.0940.26640.5700.0096128 5 – 9 Matches 108-0.0940.26640.1360.011264

25. Results: PCA

27. RMSD – Percent of Range Linear Regression: No PCALinear Regression: PCA Data SetFeaturesEuclideanPredictedEuclideanPredicted 6 – 9 Matches 12823.3%17.3%30.4%6.7% 6 – 9 Matches 6423.3%12.9%30.4%12.5% 5 – 9 Matches 12819.9%9.7%26.6%10.1% 5 – 9 Matches 6419.9%11.1%26.6%11.8%

28. Example: Nodule 37 and Nodule 38 Nodule 38Nodule 37 Euclidean Similarity Value PCA Similarity Value 0.5490660.004379 Nodule Number LobulationMalignancyMarginSphericitySpiculationSubtletyTexture 375355545 385355555

29. Future Work Perform the analysis only nodules on which all three radiologists agree In order to address the small size of the data set, perform the analysis using a leave one out technique (instead of 2/3 training and 1/3 testing) Incorporate relevance feedback into the system

30. Questions?