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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 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 Malignancy Margin Sphericity Spiculation Subtlety Texture Content-Based Features: 77 pairs with a correlation > 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 – – –

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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 – 9 Matches dist 3 5 – 9 Matches – 9 Matches dist 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 – 9 Matches – 9 Matches – 9 Matches

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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 – 9 Matches – 9 Matches – 9 Matches

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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 %17.3%30.4%6.7% 6 – 9 Matches %12.9%30.4%12.5% 5 – 9 Matches %9.7%26.6%10.1% 5 – 9 Matches %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 Nodule Number LobulationMalignancyMarginSphericitySpiculationSubtletyTexture

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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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Questions?

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