integration of radiologists feedback into computer aided diagnosis systems
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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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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 simple distance metrics
Methodology: Simple Distance Metrics

Semantic-Based Similarity

Content-Based Similarity

simple distance metrics
Simple Distance Metrics

Content-Based Similarity Values (Euclidean)

Semantic-Based Similarity Values (1 – Cosine)

methodology principle component analysis
Methodology: Principle Component Analysis
  • Content-Based Features:
  • 77 pairs with a correlation > 0.9
  • 136 pairs with a correlation > 0.8 or < -0.8
methodology principle component analysis1
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
overview2
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
future work
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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