Integration of Radiologists’ Feedback into Computer-Aided Diagnosis Systems

1. Integration of Radiologists’ Feedback into Computer-Aided Diagnosis Systems Sarah A. Jabona Daniela S. Raicub Jacob D. Furstb aRose-Hulman Institute of Technology, Terre Haute, IN 47803 bSchool 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 • 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

17. Matches: Nodule 117

18. Simple Distance Metrics

19. 5 – 9 Matches: PCA and Linear Regression

20. Results: Linear Regression

21. Results: Linear Regression

22. Results: Linear Regression

23. Results: Linear Regression

24. Results: PCA

25. Results: PCA

26. Results: PCA

27. RMSD – Percent of Range

28. Example: Nodule 37 and Nodule 38

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?