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Artificial Neural Networks in Medicine and Biology

Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Helge Malmgren

    1. Department of Philosophy, Göteborg University, Göteborg, Sweden

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  2. Magnus Borga

    1. Department of Electrical Engineering, Linköping University, Linköping, Sweden

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  3. Lars Niklasson

    1. Department of Computer Science, University of Skövde, Skövde, Sweden

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  • Comprises a selection of papers from the first international conference to focus specifically on the use of ANNs in medicine and biology
  • Provides a state-of-the-art overview of a rapidly expanding area of research interest

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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About this book

This book contains the proceedings of the conference ANNIMAB-l, held 13-16 May 2000 in Goteborg, Sweden. The conference was organized by the Society for Artificial Neural Networks in Medicine and Biology (ANNIMAB-S), which was established to promote research within a new and genuinely cross-disciplinary field. Forty-two contributions were accepted for presentation; in addition to these, S invited papers are also included. Research within medicine and biology has often been characterised by application of statistical methods for evaluating domain specific data. The growing interest in Artificial Neural Networks has not only introduced new methods for data analysis, but also opened up for development of new models of biological and ecological systems. The ANNIMAB-l conference is focusing on some of the many uses of artificial neural networks with relevance for medicine and biology, specifically: • Medical applications of artificial neural networks: for better diagnoses and outcome predictions from clinical and laboratory data, in the processing of ECG and EEG signals, in medical image analysis, etc. More than half of the contributions address such clinically oriented issues. • Uses of ANNs in biology outside clinical medicine: for example, in models of ecology and evolution, for data analysis in molecular biology, and (of course) in models of animal and human nervous systems and their capabilities. • Theoretical aspects: recent developments in learning algorithms, ANNs in relation to expert systems and to traditional statistical procedures, hybrid systems and integrative approaches.

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Table of contents (50 papers)

  1. Front Matter

    Pages I-XII
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  2. Invited Presentations

    1. Front Matter

      Pages 1-1
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    2. Protein β-Sheet Partner Prediction by Neural Networks

      • Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak
      Pages 3-9

    3. ART Neural Networks for Medical Data Analysis and Fast Distributed Learning

      • Gail A. Carpenter, Boriana L. Milenova
      Pages 10-17

    4. Modelling Uncertainty in Biomedical Applications of Neural Networks

      • Georg Dorffner, Peter Sykacek, Christian Schittenkopf
      Pages 18-25

    5. Neural Computation in Medicine: Perspectives and Prospects

      • Richard Dybowski
      Pages 26-36

    6. Discriminating Gourmets, Lovers, and Enophiles? Neural Nets Tell All About Locusts, Toads, and Roaches

      • Wayne M. Getz, William C. Lemon
      Pages 37-44

    7. An Unsupervised Learning Method that Produces Organized Representations from Real Information

      • Teuvo Kohonen
      Pages 45-53

    8. On Forgetful Attractor Network Memories

      • Anders Lansner, Anders Sandberg, Karl Magnus Petersson, Martin Ingvar
      Pages 54-62

    9. Outstanding Issues for Clinical Decision Support with Neural Networks

      • P. J. G. Lisboa, A. Vellido, H. Wong
      Pages 63-71

  3. Medical Image Analysis

    1. Front Matter

      Pages 73-73
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    2. Cancerous Liver Tissue Differentiation Using LVQ

      • Kuo-Sheng Cheng, Richard Sun, Nan-Haw Chow
      Pages 75-80

    3. Quantification of Diabetic Retinopathy using Neural Networks and Sensitivity Analysis

      • Andrew Hunter, James Lowell, Jonathan Owens, Lee Kennedy, David Steele
      Pages 81-86

    4. Internet Based Artificial Neural Networks for the Interpretation of Medical Images

      • Andreas Järund, Lars Edenbrandt, Mattias Ohlsson, Erik Borälv
      Pages 87-92

    5. Segmentation of Magnetic Resonance Images According to Contrast Agent Uptake Kinetics Using a Competitive Neural Network

      • Ross J Maxwell, John Wilson, Gillian M Tozer, Paul R Barber, Borivoj Vojnovic
      Pages 93-98

    6. Applications of Optimizing Neural Networks in Medical Image Registration

      • Anand Rangarajan, Haili Chui
      Pages 99-104

    7. A Learning by Sample Approach for the Detection of Features in Medical Images.

      • Camille Serruys, Djamel Brahmi, Alain Giron, Nathalie Cassoux, Raoul Triller, Phuc Le Huang et al.
      Pages 105-110

    8. Neural Network Based Classification of Cell Images via Estimation of Fractal Dimensions

      • Changjing Shang, Craig Daly, John McGrath, John Barker
      Pages 111-116

  4. Signal Processing in Medicine

    1. Front Matter

      Pages 117-117
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    2. Mutual Control Neural Networks for Sleep Arousal Detection

      • Theodoras Assimakopoulos, Kyra Dingli, Neil J. Douglas
      Pages 119-124
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Editors and Affiliations

  • Department of Philosophy, Göteborg University, Göteborg, Sweden

    Helge Malmgren

  • Department of Electrical Engineering, Linköping University, Linköping, Sweden

    Magnus Borga

  • Department of Computer Science, University of Skövde, Skövde, Sweden

    Lars Niklasson

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