Selection of examples in case-based computer-aided decision systems

Physics in Medicine and Biology

Volumn 53, Issue 21, 2008, Pages 6079-6096

  Mazurowski, Maciej A ^a   Zurada, Jacek M ^a   Tourassi, Georgia D ^b  

^a University of Louisville   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; ELECTRIC CIRCUIT BREAKERS; GENETIC ALGORITHMS; RANDOM PROCESSES;

BASE-LINES; CAD SYSTEMS; CASE BASE; CURRENT TRENDS; DECISION SYSTEMS; DIAGNOSTIC PERFORMANCES; DIGITAL DATABASES; EXPERIMENTAL EVALUATIONS; FALSE POSITIVES; INTELLIGENT TECHNIQUES; MEDICAL DOMAINS; RANDOM MUTATIONS; RANDOM SELECTIONS; SCREENING MAMMOGRAMS; STORAGE REQUIREMENTS;

DATABASE SYSTEMS;

ARTICLE; COMPUTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DATA BASE; GENETIC ALGORITHM; INFORMATION SYSTEM; LABORATORY DIAGNOSIS; MAMMOGRAPHY; PRIORITY JOURNAL;

COMPUTER STORAGE DEVICES; DECISION MAKING, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS;

EID: 58149216575     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/53/21/013     Document Type: Article

References (41)

1. Aha D A 1992 Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms Int. J. Man-Mach. Stud. 36 267-87
2. Aha D W, Kibler D and Albert M K 1991 Instance-based learning algorithms Mach. Learn. 6 37-66
3. Blum A L and Langley P 1997 Selection of relevant features and examples in machine learning Artif. Intell. 97 245-71
4. Bradley A P 1997 The use of the area under the roc curve in the evaluation of machine learning algorithms Pattern Recognit. 30 1145-59
5. Cano J R, Herrera F and Lozano M 2003 Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study IEEE Trans. Evol. Comput. 7 561-75
6. Catarious D M, Baydush A H and Floyd C E 2004 Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system Med. Phys. 31 1512-20
7. Chan C-L 1974 Finding prototypes for nearest neighbor classifiers IEEE Trans. Comput. 23 1179-84
8. Chang Y-H, Hardesty L A, Hakim C M, Chang T S, Zheng B, Good W F and Gur D 2001 Knowledge-based computer-aided detection of masses on digitized mammograms: preliminary assessment Med. Phys. 28 455-61
9. Cheng H, Cai X, Chen X, Hu L and Lou X 2003 Computer-aided detection and classification of microcalcifications in mammograms: a survey Pattern Recognit. 36 2967-91
10. Doi K 2005 Current status and future potential of computer-aided diagnosis in medical imaging Br. J. Radiol. 78 S3-S19
11. Doi K 2007 Computer-aided diagnosis in medical imaging: historical review, current status and future potential Comput. Med. Imaging Graph. 31 198-211
12. Duda R O, Hart P E and Stork D G 2000 Pattern Classification (New York: Wiley-Interscience)
13. Efron B and Tibshirani R J 1993 An Introduction to the Bootstrap (London: Chapman and Hall)
14. Eiben A E and Smith J E 2003 Introduction to Evolutionary Computing (Berlin: Springer)
15. El-Naqa I, Yang Y, Galatsanos N P, Nishikawa R M and Wernick M N 2004 A similarity learning approach to content-based image retrieval: application to digital mammography IEEE Trans. Med. Imaging 23 1233-44
16. Hart P E 1968 The condensed nearest neighbor rule IEEE Trans. Inf. Theory 14 515-6
17. Heath M et al 1998 Current status of the digital database for screening mammography Digital Mammography (Dordrecht: Kluwer)
18. Katsuragawa S and Doi K 2007 Computer-aided diagnosis in chest radiography Comput. Med. Imaging Graph. 31 212-23
19. Li Q, Li F, Suzuki K, Shiraishi J, Abe H, Engelmann R, Nie Y, MacMahon H and Doi K 2005 Computer-aided diagnosis in thoracic CT Semin. Ultrasound CT MRI 26 357-63
20. Llora X and Garrell J M 2003 Prototype induction and attribute selection via evolutionary algorithms Intell. Data Anal. 7 193-208
21. Lo J Y, Bilska-Wolak A O, Markey M K, Tourassi G D, Baker J A and Floyd C E Jr 2006 Computer-aided diagnosis in breast imaging: where do we go after detection? Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer ed Jasjit S Suri and Rangaraj M Rangayyan (Bellingham, WA: SPIE Press) pp 871-900
22. Mazurowski M A, Habas P A, Zurada J M and Tourassi G D 2007 Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms Proc. of IEEE Congress on Evolutionary Computation (CEC 2007) pp 600-5
23. Mazurowski M A, Habas P A, Zurada J M and Tourassi G D 2008 Decision optimization of case-based computer aided decision systems using genetic algorithms with application to mammography Phys. Med. Biol. 53 895-908
24. Metz C E, Herman B A and Roe C A 1998a Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets Med. Decis. Mak. 18 110-21
25. Metz C E, Herman B A and Shen J-H 1998b Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data Stat. Med. 17 1033-53
26. Michalewicz Z 1999 Genetic Algorithms + Data Structures = Evolutionary Programs (Berlin: Springer)
27. Mitchell T 1997 Machine Learning (New York: McGraw-Hill)
28. Obuchowski N A 2003 Receiver operating characteristic curves and their use in radiology Radiology 229 3-8
29. Park S C, Sukthankar R, Mummert L, Satyanarayanan M and Zheng B 2007 Optimization of reference library used in content-based medical image retrieval scheme Med. Phys. 34 4331-9
30. Pekalska E, Duin R P and Paclik P 2006 Prototype selection for dissimilarity-based classifiers Pattern Recognit. 39 189-208
31. Perumpillichira J J, Yoshida H and Sahani D V 2005 Computer-aided detection for virtual colonoscopy Cancer Imaging 5 11-6
32. Sampat M P, Markey M K and Bovik A C 2005 Computer-aided detection and diagnosis in mammography Handbook of Image and Video Processing ed Alan C Bovik (New York: Academic) pp 1195-217
33. Skalak D B 1994 Prototype and feature selection by sampling and random mutation hill climbing algorithms Proc. 11th Int. Conf. on Machine Learning pp 293-301
34. Sluimer I, Schilham A, Prokop M and van Ginneken B 2006 Computer analysis of computed tomography scans of the lung: a survey IEEE Trans. Med. Imaging 25 385-405
35. Tourassi G D and Floyd C E Jr 2004 Performance evaluation of an information-theoretic CAD scheme for the detection of mammographic architectural distortion Proc. SPIE 5034 59-66
36. Tourassi G D, Harrawood B, Singh S and Lo J Y 2007a Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance Med. Phys. 34 3193-204
37. Tourassi G D, Haarawood B, Singh S, Lo J Y and Floyd C E Jr 2007b Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms Med. Phys. 34 140-50
38. Tourassi G D, Vargas-Voracek R and Floyd C E Jr 2003 Content-based image retrieval as a computer aid for the detection of mammographic masses Proc. SPIE 5032 590-7
39. Tourassi G D, Vargas-Voracek R, Catarious D M Jr and Floyd C E Jr 2003 Computer-assisted detection of mammographic masses: a template matching scheme based on mutual information Med. Phys. 30 2123-30
40. Wilson R and Martinez T R 2000 Reduction techniques for instance-based learning algorithms Mach. Learn. 38 257-86
41. Zurada J M 1992 Introduction to Artificial Neural Systems (St Paul, MN: West Publishing Company)