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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. Overview. Introduction

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Integration of radiologists feedback into computer aided diagnosis systems

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
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
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
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 work1
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
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


Overview1
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
Methodology Diagnosis Systems


Methodology simple distance metrics
Methodology: Simple Distance Metrics Diagnosis Systems

Semantic-Based Similarity

Content-Based Similarity


Simple distance metrics
Simple Distance Metrics Diagnosis Systems

Content-Based Similarity Values (Euclidean)

Semantic-Based Similarity Values (1 – Cosine)



Methodology principle component analysis
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
Scree Plots: 5 – 9 Matches Diagnosis Systems


Methodology principle component analysis1
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


Overview2
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



Matches nodule 117
Matches: Nodule 117 Diagnosis Systems


Simple distance metrics1
Simple Distance Metrics Diagnosis Systems



Results linear regression
Results: Linear Regression Diagnosis Systems


Results linear regression1
Results: Linear Regression Diagnosis Systems


Results linear regression2
Results: Linear Regression Diagnosis Systems


Results linear regression3
Results: Linear Regression Diagnosis Systems


Results pca
Results: PCA Diagnosis Systems


Results pca1
Results: PCA Diagnosis Systems


Results pca2
Results: PCA Diagnosis Systems


Rmsd percent of range
RMSD – Percent of Range Diagnosis Systems



Future work
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
Questions? Diagnosis Systems


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