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Integration of Radiologists’ Feedback into Computer-Aided Diagnosis SystemsPowerPoint Presentation

Integration of Radiologists’ Feedback into Computer-Aided Diagnosis Systems

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### 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

Overview Diagnosis Systems

- 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

Introduction Diagnosis Systems

- 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

Related Work Diagnosis Systems

- 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

Related Work Diagnosis Systems

- 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)

The Data Diagnosis Systems

- 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

Overview Diagnosis Systems

- 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

Methodology Diagnosis Systems

Methodology: Simple Distance Metrics Diagnosis Systems

Semantic-Based Similarity

Content-Based Similarity

Simple Distance Metrics Diagnosis Systems

Content-Based Similarity Values (Euclidean)

Semantic-Based Similarity Values (1 – Cosine)

Methodology: Linear Regression Diagnosis Systems

Methodology: Principle Component Analysis Diagnosis Systems

- Content-Based Features:
- 77 pairs with a correlation > 0.9
- 136 pairs with a correlation > 0.8 or < -0.8

Scree Plots: 5 – 9 Matches Diagnosis Systems

Methodology: Principle Component Analysis Diagnosis Systems

- 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

- Method 1

Overview Diagnosis Systems

- 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

Results: Simple Distance Metric Diagnosis Systems

Matches: Nodule 117 Diagnosis Systems

Simple Distance Metrics Diagnosis Systems

5 – 9 Matches: PCA and Linear Regression Diagnosis Systems

Results: Linear Regression Diagnosis Systems

Results: Linear Regression Diagnosis Systems

Results: Linear Regression Diagnosis Systems

Results: Linear Regression Diagnosis Systems

Results: PCA Diagnosis Systems

Results: PCA Diagnosis Systems

Results: PCA Diagnosis Systems

RMSD – Percent of Range Diagnosis Systems

Example: Nodule 37 and Nodule 38 Diagnosis Systems

Future Work Diagnosis Systems

- 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

Questions? Diagnosis Systems

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