Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

Abstract

The goal of this project is to develop a computer-aided diagnosis (CAD) system for mass detection using advanced computer vision techniques that will be trained with retrospectively detected cancers on prior mammograms. The new CAD system will be combined with our existing CAD system. When fully developed, the new dual CAD system should increase the sensitivity of detecting cancers at the early stage without compromising the sensitivity for other cancers. During this project year, we have performed the following tasks: (1) continue to collect the data sets of digitized film mammograms for testing our CAD system, (2) investigation of a bilateral approach to reduce the false positives (FPs) on single CAD system, (3) develop image processing techniques for improvement of mass detection on prior mammograms, and (4) continue to develop a two-view information fusion method to improve the performance of single CAD system. In summary, we have investigated a number of areas in CAD of mammographic masses and evaluated the new techniques for mass detection on mammograms. We have made progress in three of the tasks proposed in the project. We have found that our new computer-vision techniques can improve the performance of the CAD systems. We will continue the development of the CAD system in the coming years.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA472884

Entities

People

  • Jun Wei

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Biomedical Research
  • Breast Cancer
  • Computer Vision
  • Computer-Aided Diagnosis
  • Computers
  • Data Sets
  • Detection
  • Detectors
  • Feature Extraction
  • Health Services
  • Identification
  • Image Processing
  • Institutional Review Board
  • Medical Personnel
  • Neural Networks
  • Pattern Recognition
  • Recognition

Fields of Study

  • Medicine
  • Physics

Readers

  • Oncology and Biomarker-Based Cancer Detection.
  • Systems Analysis and Design

Technology Areas

  • AI & ML