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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

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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

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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

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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

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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)

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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

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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

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Methodology

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

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

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Methodology: Linear Regression

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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

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Scree Plots: 5 – 9 Matches

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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

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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

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

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Matches: Nodule 117

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Simple Distance Metrics

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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

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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

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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

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Results: Linear Regression

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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

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Results: PCA

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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%

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

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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

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