Uncertainty in the output of artificial neural networks

IEEE Transactions on Medical Imaging

Volumn 22, Issue 7, 2003, Pages 913-921

  Jiang, Yulei ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Artificial neural networks; Classification; Computer aided diagnosis; Estimation uncertainty; Prediction error

Indexed keywords

COMPUTER AIDED DIAGNOSIS; ERROR ANALYSIS; ITERATIVE METHODS; NEURAL NETWORKS;

PREDICTION ERRORS;

MAMMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; FEMALE; HUMAN; MAMMOGRAPHY; METHODOLOGY; QUALITY CONTROL; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; VALIDATION STUDY;

ALGORITHMS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAMMOGRAPHY; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); QUALITY CONTROL; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 0042025303     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2003.815061     Document Type: Article

References (21)

1. J. M. Boone, G. W. Gross, and V. Greco-Hunt, "Neural networks in radiologic diagnosis - I: Introduction and illustration," Investigat. Radiol., vol. 25, pp. 1012-1016, 1990.
2. Y. Wu, K. Doi, C. E. Metz, N. Asada, and M. L. Giger, "Simulation studies of data classification by artificial neural networks: Potential applications in medical imaging and decision making," J. Digit. Imag., vol. 6, pp. 117-125, 1993.
3. Y. Wu, M. L. Giger, K. Doi, C. J. Vyborny, R. A. Schmidt, and C. E. Metz, "Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer," Radiology, vol. 187, pp. 81-87, 1993.
4. M. A. Kupinski, D. C. Edwards, M. L. Giger, and C. E. Metz, "Ideal observer approximation using Bayesian classification neural networks," IEEE Trans. Med. Imag., vol. 20, pp. 886-899, Sept. 2001.
5. J. A. Baker, P. J. Kornguth, J. Y. Lo, and C. E. J. Floyd, "Artificial neural network: Improving the quality of breast biopsy recommendations," Radiology, vol. 198, pp. 131-135, 1996.
6. H. P. Chan, B. Sahiner, N. Petrick, M. A. Helvie, K. L. Lam, D. D. Adler, and M. M. Goodsitt, "Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network," Phys. Med. Biol., vol. 42, pp. 549-567, 1997.
7. Y. Jiang, R. M. Nishikawa, R. A. Schmidt, C. E. Metz, M. L. Giger, and K. Doi, "Improving breast cancer diagnosis with computer-aided diagnosis," Acad. Radiol., vol. 6, pp. 22-33, 1999.
8. Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz, "Breast cancer: Effectiveness of computer-aided diagnosis observer study with independent database of mammograms," Radiology, vol. 224, pp. 560-568, 2002.
9. K. Ashizawa, H. MacMahon, T. Ishida, K. Nakamura, C. J. Vyborny, S. Katsuragawa, and K. Doi, "Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs," Amer. J. Roentgenol., vol. 172, pp. 1311-1315, 1999.
10. P. A. Lachenbruch and M. R. Mickey, "Estimation of error rates in discriminant analysis," Technometrics, vol. 10, pp. 1-11, 1968.
11. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. Boston, MA: Academic, 1990.
12. H. P. Chan, B. Sahiner, R. F. Wagner, and N. Petrick, "Classifier design for computer-aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers," Med. Phys., vol. 26, pp. 2654-2668, 1999.
13. W. S. Sarle, "Stopped training and other remedies for overfilling," in Proc. 27th Symp. Interface, 1995, pp. 352-360.
14. C. E. Metz, "ROC methodology in radiologic imaging," Investigat. Radiol., vol 21, pp. 720-733, 1986.
15. J. A. Swets, "Measuring the accuracy of diagnostic systems," Science, vol. 240, pp. 1285-1293, 1988.
16. J. A. Hanley and B. J. McNeil, "The meaning and use of the area under a receiver operating characteristic (ROC) curve," Radiology, vol. 143, pp. 29-36, 1982.
17. C. E. Metz, "Some practical issues of experimental design and data analysis in radiological ROC studies," Investigat. Radiol., vol. 24, pp. 234-245, 1989.
18. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in Microstructure of Cognition. Volume 1: Foundations, D. E. Rumelhart, J. L. McClell, and T. P. R. Group, Eds. Cambridge, MA: The MIT Press, 1986, vol. 1, Computational model of cognition and perception, pp. 318-362.
19. Y. Jiang, R. M. Nishikawa, D. E. Wolverton, C. E. Metz, M. L. Giger, R. A. Schmidt, C. J. Vyborny, and K. Doi, "Malignant and benign clustered microcalcifications: Automated feature analysis and classification," Radiology, vol. 198, pp. 671-678, 1996.
20. R. Tibshirani, "A comparison of some error estimates for neural network models," Neural Comput., vol. 8, pp. 152-163, 1996.
21. G. Papadopoulos, P. J. Edwards, and A. F. Murray, "Confidence estimation methods for neural networks: A practical comparison," IEEE Trans. Neural Networks, vol. 12, pp. 1278-1287, Nov. 2001.