Abstract

During the past several years, many CAD schemes in mammography were developed and tested with varying degrees of success. For the most part, these systems have been evaluated by measuring performance on limited sets of data, and in some cases, on the training set itself, with little effort to assess how performance would be effected if the algorithms were applied to a large data base or preferably the general image ensemble. Since the diagnostic performance of some CAD systems is approaching the point where clinical utility can be demonstrated, it is becoming increasingly important to explore ways not only to optimize these schemes, but also to assess their robustness when used in the clinical environment. In this project, we propose to conduct a variety of studies to improve and test the performance and robustness of two of our own CAD schemes. We will also assess a number of issues associated with system's performance when multiple images (e.g., views) and/or multiple independent schemes are available and are used to optimize the results of a diagnostic examination.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA077850-01
Application #
2616058
Study Section
Special Emphasis Panel (ZRG7-DMG (01))
Project Start
1997-09-15
Project End
2001-06-30
Budget Start
1997-09-15
Budget End
1998-06-30
Support Year
1
Fiscal Year
1997
Total Cost
Indirect Cost

  Institution

Name
Allegheny University of Health Sciences
Department
Type
Other Domestic Higher Education
DUNS #
City
Philadelphia
State
PA
Country
United States
Zip Code
19129

  Publications

Wang, Xingwei; Li, Lihua; Liu, Wei et al. (2012) An interactive system for computer-aided diagnosis of breast masses. J Digit Imaging 25:570-9
Zheng, Bin; Sumkin, Jules H; Zuley, Margarita L et al. (2012) Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol 81:3222-8
Zheng, B; Sumkin, J H; Zuley, M L et al. (2012) Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment. Br J Radiol 85:e153-61
Wang, Xingwei; Li, Lihua; Xu, Weidong et al. (2012) Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method. Phys Med Biol 57:561-75
Wang, Xingwei; Li, Lihua; Xu, Weidong et al. (2012) Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment. Acad Radiol 19:303-10
Wang, Xingwei; Lederman, Dror; Tan, Jun et al. (2011) Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry. Med Eng Phys 33:934-42
Wang, Xiao Hui; Park, Sang Cheol; Zheng, Bin (2011) Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database. J Digit Imaging 24:352-9
Zheng, Bin; Wang, Xingwei; Lederman, Dror et al. (2010) Computer-aided detection; the effect of training databases on detection of subtle breast masses. Acad Radiol 17:1401-8
Wang, Xingwei; Lederman, Dror; Tan, Jun et al. (2010) Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification. Acad Radiol 17:1234-41
Park, Sang Cheol; Pu, Jiantao; Zheng, Bin (2009) Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers. Acad Radiol 16:266-74

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