Skip to main content
SpringerLink
Log in

Evaluating a neural network decision-support tool for the diagnosis of breast cancer

  • Conference paper
  • First Online:
Artificial Intelligence in Medicine (AIME 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 934))

Included in the following conference series:

Abstract

This paper describes the evaluation of an application of the ARTMAP neural network model to the diagnosis of cancer from fine-needle aspirates of the breast. The network has previously demonstrated very high performance when used with high-quality data provided by an expert pathologist. New performance results are provided for its use with “noisy” data provided by an inexperienced pathologist. Additionally, ARTMAP supports the extraction of symbolic rules from a trained network and the validity of these autonomously-acquired rules is discussed. It is concluded that the symbolic rules provide an appropriate mapping of input features to category classes in the domain. However, the network in its present form is only suitable for use as a decision-support tool by a senior pathologist, since its performance deteriorated greatly with poor-quality data provided by a junior pathologist. The implications of the findings are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G.A. Carpenter and S. Grossberg (1987) A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine, Computer Vision, Graphics and Image Processing, 37, 54–115.

    Google Scholar 

  2. G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds and D.B. Rosen (1992) Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps, IEEE Transactions on Neural Networks, 3(5), 698–712.

    Article  Google Scholar 

  3. G.A. Carpenter, S. Grossberg and J.H. Reynolds (1991) ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network, Neural Networks, 4(5), 565–588.

    Article  Google Scholar 

  4. G.A. Carpenter and A.H. Tan (1993) Rule Extraction, Fuzzy ARTMAP, and Medical Databases, Proceedings of the World Congress on Neural Networks, Volume I, 501–506.

    Google Scholar 

  5. S.S. Cross, T.J. Stephenson, Y. Diez, R.F. Harrison, J.C.E. Underwood and J. Downs (In Press) Diagnosis of Breast Fine Needle Aspirates Using Human Observations and a Multi-Layer Perceptron Neural Network, to appear in J. Pathol..

    Google Scholar 

  6. G. Cybenko (1989) Approximations by Superpositions of a Sigmoidal Function, Mathematics of Control, Signals and Systems, 2, 303–314.

    Google Scholar 

  7. J. Downs, R.F. Harrison and S.S. Cross (In Press) A Neural Network Decision Support Tool for the Diagnosis of Breast Cancer, to appear in Proceedings of the 10th Conference of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB-95). Amsterdam: IOS Press.

    Google Scholar 

  8. J. Downs, R.F. Harrison and R.L. Kennedy (1995) A Prototype Neural Network Decision Support Tool for the Diagnosis of Acute Myocardial Infarction, this volume.

    Google Scholar 

  9. S.A.C. Dundas, P.R. Sanderson, H. Matta and A.J. Shorthouse (1988) Fine Needle Aspiration of Palpable Breast Lesions: Results Obtained with Cytocentrifuge Preparation of Aspirates, Acta Cytologica, 32, 202–206.

    PubMed  Google Scholar 

  10. C.W. Elston and I.O. Ellis (1990) Pathology and Breast Screening, Histopathology, 16, 109–118.

    PubMed  Google Scholar 

  11. P.W. Hamilton, N. Anderson, P.H. Bartels and D. Thompson (1994) Expert System Support Using Bayesian Belief Networks in the Diagnosis of Fine Needle Aspiration Biopsy Specimens of the Breast, J. Clin. Pathol., 47, 329–336.

    PubMed  Google Scholar 

  12. R.F. Harrison, C.P. Lim and R.L. Kennedy (1994) Autonomously Learning Neural Networks for Clinical Decision Support. In: E.C. Ifeachor and K.G. Rosen, (Eds.) Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED-94), Plymouth, UK, 15–22.

    Google Scholar 

  13. F. Hayes-Roth, D.A. Waterman and D.B. Lenat (1983) Building Expert Systems. London: Addison-Wesley.

    Google Scholar 

  14. H.A. Heathfield, N. Kirkham, I.O. Ellis and G. Winstanley (1990) Computer Assisted Diagnosis of Fine Needle Aspirates of the Breast, J. Clin. Pathol., 43, 168–170.

    PubMed  Google Scholar 

  15. C.P. Lim and R.F. Harrison (In Press) Modified Fuzzy ARTMAP Approaches Bayes Optimal Classification Rates: An Empirical Demonstration, to appear in Neural Networks.

    Google Scholar 

  16. J. Moody and C. Darken (1989) Fast Learning in Networks of Locally-Tuned Processing Units, Neural Computation, 1, 281–294.

    Google Scholar 

  17. J. Park and I. Sandberg (1991) Universal Approximation Using Radial Basis Function Networks, Neural Computation, 3, 246–257.

    Google Scholar 

  18. J.R. Quinlan (1986) Induction of Decision Trees, Machine Learning, 1, 81–106.

    Google Scholar 

  19. D. Rumelhart, G. Hinton and R. Williams (1986) Learning Representations by Back-Propagating Errors, Nature, 323, 533–536.

    Google Scholar 

  20. P.B. Silcocks (1983) Measuring Repeatability and Validity of Histological Diagnosis-A Brief Review with Some Practical Examples, J. Clin. Pathol., 36, 1269–1275.

    PubMed  Google Scholar 

  21. R.D. Start, P.B. Silcocks, S.S. Cross and J.H.F. Smith (1992) Problems with Audit of a New Fine-Needle Aspiration Service in a District General Hospital, J. Pathol., 167, 141A.

    Google Scholar 

  22. G.Towell and J.W. Shavlik (1993) Extracting Refined Rules from Knowledge-Based Neural Networks, Machine Learning, 13(1), 71–101.

    Google Scholar 

  23. J.C.E. Underwood (1992) Tumours: Benign and Malignant. In: J.C.E. Underwood, (Ed.) General and Systematic Pathology, 223–246. Edinburgh: Churchill Livingstone.

    Google Scholar 

  24. CA.Wells, I.O. Ellis, H.D. Zakhour and A.R. Wilson (1994) Guidelines for Cytology Procedures and Reporting on Fine Needle Aspirates of the Breast, Cytopathology, 5, 316–334.

    PubMed  Google Scholar 

  25. W.H. Wolberg and O.L. Mangasarian (1993) Computer-Designed Expert Systems for Breast Cytology Diagnosis, Anal. Quant. Cytol. Histol., 15, 67–74.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Joseph Downs & Robert F Harrison

  2. Dept. of Pathology, University of Sheffield Medical School, Beech Hill Road, S10 2RX, Sheffield, UK

    Simon S Cross

Authors
  1. Joseph Downs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert F Harrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon S Cross
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Pedro Barahona Mario Stefanelli Jeremy Wyatt

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Downs, J., Harrison, R.F., Cross, S.S. (1995). Evaluating a neural network decision-support tool for the diagnosis of breast cancer. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_141

Download citation

  • DOI: https://doi.org/10.1007/3-540-60025-6_141

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60025-1

  • Online ISBN: 978-3-540-49407-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation