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.

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

Zusammenfassung
This volume comprises a selection of papers focusing specifically on the topics of ANNs in medicine and biology. It covers three main areas: the medical applications of ANNs, such as in diagnosis and outcome prediction, medical image analysis, and medical signal processing.

Inhalt
Invited Presentations.- Protein ?-Sheet Partner Prediction by Neural Networks.- ART Neural Networks for Medical Data Analysis and Fast Distributed Learning.- Modelling Uncertainty in Biomedical Applications of Neural Networks.- Neural Computation in Medicine: Perspectives and Prospects.- Discriminating Gourmets, Lovers and Enophiles? Neural Nets Tell All About Locusts, Toads, and Roaches.- An Unsupervised Learning Method that Produces Organized Representations from Real Information.- On Forgetful Attractor Network Memories.- Outstanding Issues for Clinical Decision Support with Neural Networks.- Medical Image Analysis.- Cancerous Liver Tissue Differentiation Using LVQ.- Quantification of Diabetic Retinopathy Using Neural Networks and Sensitivity Analysis.- Internet Based Artificial Neural Networks for the Interpretation of Medical Images.- Segmentation of Magnetic Resonance Images According to Contrast Agent Uptake Kinetics Using a Competitive Neural Network.- Applications of Optimizing Neural Networks in Medical Image Registration.- A Learning by Sample Approach for the Detection of Features in Medical Images.- Neural Network Based Classification of Cell Images via Estimation of Fractal Dimensions.- Signal Processing in Medicine.- Mutual Control Neural Networks for Sleep Arousal Detection.- Extraction of Sleep-Spindles from the Electroencephalogram (EEG).- Analyzing Brain Tumor Related EEG Signals with ICA Algorithms.- Isolating Seizure Activity in the EEG with Independent Component Analysis.- Seizure Detection with the Self-Organising Feature Map.- Graphical Analysis of Respiration in Postoperative Patients Using Self Organising Maps.- Clinical Diagnosis and Medical Decision Support.- Neural Network Predictions of Outcome from Posteroventral Pallidotomy.- SurvivalAnalysis: A Neural-Bayesian Approach.- Identifying Discriminant Features in the Histopathology Diagnosis of Inflammatory Bowel Disease Using a Novel Variant of the Growing Cell Structure Network Technique.- Classifying Pigmented Skin Lesions with Machine Learning Methods.- An Assessment System of Dementia of Alzheimer Type Using Artificial Neural Networks.- A New Artificial Neural Network Method for the Interpretation of ECGs.- Use of a Kohonen Neural Network to Characterize Respiratory Patients for Medical Intervention.- Determination of Microalbuminuria and Increased Urine Albumin Excretion by Immunoturbidimetric Assay and Neural Networks.- Artificial Neural Networks to Predict Postoperative Nausea and Vomiting.- Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks.- Bayesian Neural Networks Used to Find Adverse Drug Combinations and Drug Related Syndromes.- Monitoring of Physiological Parameters of Patients and Therapists During Psychotherapy Sessions Using Self-Organizing Maps.- Biomolecular Applications and Biological Modelling.- Neuronal Network Modelling of the Somatosensory Pathway and its Application to General Anaesthesia.- A Hybrid Classification Tree and Artificial Neural Network Model for Predicting the In Vitro Response of the Human Immunodeficiency Virus (HIV1) to Anti-Viral Drug Therapy.- Neural Unit Sensitive to Modulation.- On Methods for Combination of Results from Gene-Finding Programs for Improved Prediction Accuracy.- A Simulation Model for Activated Sludge Process Using Fuzzy Neural Network.- A General Method for Combining Predictors Tested on Protein Secondary Structure Prediction.- A Three-Neuron Model of Information Processing During Bayesian Foraging.- Sensorimotor Sequential Learning by a Neural Network Based onRedefined Hebbian Learning.- On Synaptic Plasticity: Modelling Molecular Kinases involved in Transmitter Release.- Self-Organizing Networks for Mapping and Clustering Biological Macromolecules Images.- Neural Network Model for Muscle Force Control Based on the Size Principle and Recurrent Inhibiton of Renshaw Cells.- Prediction of Photosensitizers Activity in Photodynamic Therapy Using Artificial Neural Networks: A 3D-QSAR Study.- Learning Methods and Hybrid Algorithms.- Case-Based Explanation for Artificial Neural Nets.- Double Growing Neural Gas for Disease Diagnosis.- The Use of a Knowledge Discovery Method for the Development of a Multi-Layer Perceptron Network that Classifies Low Back Pain Patients.- Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance.- Particle Swarm Optimisation in Feedforward Neural Network.- Author Index.