Performance gain in computer-assisted detection schemes by averaging scores generated from artificial neural networks with adaptive filtering

Medical Physics

Volumn 28, Issue 11, 2001, Pages 2302-2308

  Zheng, B ^a   Chang, Y H ^a   Good, W F ^a   Gur, D ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Adaptive filtering; Artificial neural network; Computer assisted diagnosis; Genetic algorithm; Mammography; Mass detection

Indexed keywords

ADAPTIVE FILTERING; ADAPTIVE FILTERS; COMPUTER NETWORKS; CURVE FITTING; GENETIC ALGORITHMS; MAMMOGRAPHY; NEURAL NETWORKS; X RAY SCREENS;

COMPUTER ASSISTED DETECTION; COMPUTER ASSISTED DIAGNOSIS; DIGITIZED MAMMOGRAMS; MASS DETECTION; NEGATIVE REGIONS; PERFORMANCE GAIN; TRAINING AND TESTING; TRAINING DATA SETS;

COMPUTER AIDED DIAGNOSIS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; COMPUTER ANALYSIS; CORRELATION FUNCTION; DATA BASE; FILTRATION; HUMAN; MAMMOGRAPHY; PERFORMANCE; PRIORITY JOURNAL; SCORING SYSTEM;

EID: 0035180695     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.1412240     Document Type: Article

References (45)

1. Computer-aided mammographic screening for spiculated lesions
2. Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network
3. Computer-assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms
4. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency
5. Computer-aided diagnosis: Automatic detection of malignant masses in digitized mammograms
6. Optimally weighted wavelet transform based on supervised training for the detection of microcalcifications in digital mammograms
7. A novel approach to microcalcification detection using fuzzy logic technique
8. Automatic detection of clustered microcalcifications in digitized mammogram films
9. Segmentation of suspicious clustered microcalcifications in mammograms
10. Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: An assessment
11. Computer vision and artificial intelligence in mammography
12. For the proposition, at point/counterpoint of in the next decade automated computer analysis will be an accepted sole method to separate 'normal' from 'abnormal' radiological images
13. Potential contribution of computer-aided detection to the sensitivity of screening mammography
14. Automated detection of breast carcinomas not detected in a screening program
15. Against the proposition, at point/counterpoint of in the next decade automated computer analysis will be an accepted sole method to separate 'normal' from 'abnormal' radiological images
16. False-positive reduction technique for detection of masses on digital mammograms: Global and local multiresolution texture analysis
17. Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms
18. Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue
19. A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms
20. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms
21. Incorporation of a set enumeration tree-based classifier into a hybrid computer-assisted diagnosis scheme for mass detection
22. Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm
23. Knowledge-based computer-aided detection of masses on digitized mammograms: A preliminary assessment
24. Adequacy testing of training set sample size in the development of a computer-assisted diagnosis scheme
25. Effect of case selection on the performance of computer-aided detection schemes
26. Adaptive computer-aided diagnosis scheme of digitized mammograms
27. Digital mammography: Comparison of adaptive and nonadaptive CAD schemes for mass detection
28. Gains in accuracy from replicated readings of diagnostic images: Prediction and assessment in terms of ROC analysis
29. Measuring performance efficiency and consistency in visual discriminations with noisy images
30. Computeraided diagnosis in full digital mammography
31. Clinical results with the R2 ImageCheck Mammographic CAD system
32. Soft-display mammographic readings under different computer-assisted detection cueing environments: Preliminary findings
33. Computerized detection of masses in digitized mammograms using single-image segmentation and a multiplayer topographic feature analysis
34. Variations in measured performance of CAD schemes due to database composition and scoring protocol
35. Lesion conspicuity, structure noise, and film reader error
36. Densitometric measurements of lung nodules on chest radiographs
37. Assessment of mass detection using tissue background information as input to a computerassisted diagnosis scheme
38. Computerized detection of mammographic lesions: Performance of artificial neural network with enhanced feature extraction
39. Comparison of artificial neural network and Bayesian belief network in a computer-assisted diagnosis scheme for mammography
40. Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data
41. Observer variation and the performance accuracy gained by averaging ratings of abnormality
42. Explanation and artificial neural networks
43. Feature selection with limited databases
44. Classifier design for computer-aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers