Artificial neural networks in biomedicine
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Bibliographic Information
Artificial neural networks in biomedicine
（Perspectives in neural computing）
Springer, c2000
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Includes bibliographical references and index
Description and Table of Contents
Description
Following the intense research activIties of the last decade, artificial neural networks have emerged as one of the most promising new technologies for improving the quality of healthcare. Many successful applications of neural networks to biomedical problems have been reported which demonstrate, convincingly, the distinct benefits of neural networks, although many ofthese have only undergone a limited clinical evaluation. Healthcare providers and developers alike have discovered that medicine and healthcare are fertile areas for neural networks: the problems here require expertise and often involve non-trivial pattern recognition tasks - there are genuine difficulties with conventional methods, and data can be plentiful. The intense research activities in medical neural networks, and allied areas of artificial intelligence, have led to a substantial body of knowledge and the introduction of some neural systems into clinical practice. An aim of this book is to provide a coherent framework for some of the most experienced users and developers of medical neural networks in the world to share their knowledge and expertise with readers.
Table of Contents
Tutorial and Review.- 1 The Bayesian Paradigm: Second Generation Neural Computing.- 1.1 Introduction.- 1.2 Theory.- 1.2.1 Bayesian Learning.- 1.2.2 The Evidence Framework.- 1.2.2.1 Error bars.- 1.2.2.2 Moderated outputs.- 1.2.2.3 Regularisation.- 1.2.3 Committees.- 1.3 Example Results.- 1.4 Conclusion.- 2 The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease.- 2.1 Introduction.- 2.2 Diagnosis of Disease.- 2.3 Outcome Prediction.- 2.4 Conclusion.- 3 Genetic Evolution of Neural Network Architectures.- 3.1 Introduction.- 3.2 Stability: The 'Bias/Variance Problem'.- 3.3 Genetic Algorithms and Artificial Neural Networks.- 3.3.1 Description of a General Method for Evolving ANN Architecture (EANN).- 3.3.2 Prediction of Depression After Mania.- 3.3.3 EANN and the Agreement/Transparency Choice.- 3.3.4 ANN and the Stability/Specialisation Choice.- 3.4 Conclusion.- Computer Aided Diagnosis.- 4 The Application of PAPNET to Diagnostic Cytology.- 4.1 Introduction.- 4.2 First Efforts at Automation in Cytology.- 4.3 Neural Networks.- 4.4 The PAPNET System.- 4.4.1 Components of the PAPNET System.- 4.4.1.1 Technical factors affecting the performance of the machine.- 4.4.2 Performance of the PAPNET System.- 4.4.2.1 Cervicovaginal smears.- 4.4.3 Application of the PAPNET System to Smears of Sputum.- 4.4.4 Application of the PAPNET System to Smears of Urinary Sediment.- 4.4.5 Application of the PAPNET System to Oesophageal Smears.- 4.5 Comment.- 5 ProstAsure Index - A Serum-Based Neural Network-Derived Composite Index for Early Detection of Prostate Cancer.- 5.1 Introduction.- 5.2 Clinical Background of Prostate Cancer and Derivation of the ProstAsure Index Algorithm.- 5.3 Validation of PI with Independent Clinical Data.- 5.4 Issues in Developing PI.- 5.5 Conclusion.- 6 Neurometric Assessment of Adequacy of Intraoperative Anaesthetic.- 6.1 Intraoperative Awareness.- 6.2 Measuring Sensory Perception.- 6.3 Clinical Data.- 6.4 Results.- 6.5 Implementation.- 6.6 Clinical Deployment.- 6.7 Healthcare Benefit.- 6.8 Additional Studies.- 7 Classifying Spinal Measurements Using a Radial Basis Function Network.- 7.1 Introduction.- 7.2 Data.- 7.2.1 The Spines.- 7.2.2 The Measurements.- 7.2.3 Preprocessing the Data.- 7.3 Radial Basis Functions and Networks.- 7.4 Matrix Notation.- 7.5 Training RBF Networks.- 7.5.1 The Unsupervised Learning Stage.- 7.5.2 The Supervised Learning Stage.- 7.5.2.1 Regularisation as an aid to avoid over-fitting.- 7.5.2.2 Calculating the regularisation coefficients and the weights.- 7.5.2.3 Forward subset selection of RBFs.- 7.5.2.4 Input feature selection.- 7.6 Results.- 7.7 Conclusion.- 8 GEORGIA: An Overview.- 8.1 Introduction.- 8.2 The Medical Decision Support System.- 8.3 Learning Pattern Generation.- 8.4 Software and Hardware Implementation.- 8.5 Re-Training and Re-Configuring the MDSS.- 8.6 Introducing GEORGIA's Man-to-Computer Interface.- 8.7 Conclusion.- 9 Patient Monitoring Using an Artificial Neural Network.- 9.1 Overview of the Medical Context.- 9.2 Basic Statistical Appraisal of Vital Function Data.- 9.3 Neural Network Details.- 9.3.1 Default Training.- 9.4 Implementation.- 9.5 Clinical Trials.- 9.6 Clinical Practice.- 10 Benchmark of Approaches to Sequential Diagnosis.- 10.1 Introduction.- 10.2 Preliminaries.- 10.3 Methods.- 10.3.1 The Probabilistic Algorithm.- 10.3.1.1 The diagnostic algorithm for first order markov chains - the Markov I algorithm.- 10.3.1.2 The diagnostic algorithm for second order markov chains - the Markov II algorithm.- 10.3.2 The Fuzzy Methods.- 10.3.2.1 The algorithm without context - fuzzy 0.- 10.3.2.2 The algorithm with first-order context - fuzzy lA.- 10.3.2.3 The reduced algorithm with first-order context - fuzzy 1B.- 10.3.2.4 The algorithm with second-order context - fuzzy 2A.- 10.3.2.5 The reduced algorithm with second-order context - fuzzy 2B.- 10.3.3 The Neural Network Approach.- 10.4 A Practical Example - Comparative Analysis of Methods.- 10.5 Conclusion.- 11 Application of Neural Networks in the Diagnosis of Pathological Speech.- 11.1 Introduction.- 11.2 The Research Material and the Problems Considered.- 11.2.1 Dental Prosthetics.- 11.2.2 Maxillofacial Surgery.- 11.2.3 Orthodontics.- 11.2.4 Laryngology.- 11.3 The Signal Parameterisation.- 11.4 The Application of the Neural Networks and the Results.- 11.5 Conclusion.- Signal Processing.- 12 Independent Components Analysis.- 12.1 Introduction.- 12.2 Theory.- 12.2.1 The Decorrelating Manifold.- 12.2.2 The Choice of Non-Linearity.- 12.2.3 Model-Order Estimation.- 12.3 Non-Stationary ICA.- 12.3.1 Illustration.- 12.4 Applications.- 12.4.1 Source Separation.- 12.4.2 Source Number and Estimation.- 12.5 Conclusion.- 13 Rest EEG Hidden Dynamics as a Discriminant for Brain Tumour Classification.- 13.1 Introduction.- 13.2 Characterising Hidden Dynamics.- 13.3 The Clinical Study.- 13.4 The Minimum Markov Order.- 13.5 Conclusion.- 14 Artifical Neural Network Control on Functional Electrical Stimulation Assisted Gait for Persons with Spinal Cord Injury.- 14.1 Introduction.- 14.2 Methods.- 14.3 Results.- 14.4 Discussion.- 15 The Application of Neural Networks to Interpret Evoked Potential Waveforms.- 15.1 Introduction.- 15.2 The Medical Conditions Studied.- 15.3 The Evoked Potentials.- 15.4 The Relationship Between the CNV and the Medical Conditions.- 15.5 Experimental Procedures.- 15.6 Data Pre-Processing.- 15.7 Feature Extraction.- 15.8 Normalisation.- 15.9 The Artificial Neural Networks.- 15.9.1 The Simplified Fuzzy ARTMAP.- 15.9.2 The Probabilistic Simplified Fuzzy ARTMAP.- 15.9.3 ANN Training and Accuracy.- 15.9.3.1 Small numbers of training vectors.- 15.9.3.2 Simplified fuzzy ARTMAP.- 15.9.3.3 Committees of ANNs.- 15.10 Validation Issues.- 15.10.1 Technical Aspects of Validation.- 15.10.2 Clinical Aspects of Validation.- 15.11 Results.- 15.12 Implementation Considerations.- 15.13 Future Developments.- Image Processing.- 16 Intelligent Decision Support Systems in the Cytodiagnosis of Breast Carcinoma.- 16.1 Introduction.- 16.2 Previous Work on Decision Support in this Domain.- 16.3 The Data Set in this Study.- 16.3.1 Study Population.- 16.3.2 Input Variables.- 16.3.3 Partitioning of the Data.- 16.4 Human Performance.- 16.5 Logistic Regression.- 16.6 Data Derived Decision Tree.- 16.7 Multi-Layer Perceptron Neural Networks.- 16.8 Adaptive Resonance Theory Mapping (ARTMAP) Neural Networks.- 16.8.1 Potential Advantages of ARTMAP.- 16.8.2 ARTMAP Architecture and Methodology.- 16.8.3 Results from the Cascaded System.- 16.8.4 Symbolic Rule Extraction.- 16.9 Assessment of the Different Decision Support Systems.- 17 A Neural-Based System for the Automatic Classificaton and Follow-Up of Diabetic Retinopathies.- 17.1 Introduction.- 17.2 The DRA System.- 17.3 Hybrid Module.- 17.4 Committee Algorithms.- 17.4.1 New Selection Algorithms.- 17.4.1.1 Greedy selection.- 17.4.1.2 Pseudo-exhaustive selection.- 17.4.2 Sequential Cooperation.- 17.4.3 Experimental Results.- 17.5 Related Work.- 17.6 Validation of the DRA System.- 17.7 Conclusion.- 18 Classification of Chromosomes: A Comparative Study of Neural Network and Statistical Approaches.- 18.1 Introduction.- 18.1.1 Chromosome Analysis and its Applications.- 18.1.2 Chromosome Classification.- 18.1.3 Experimental Data.- 18.2 The Neural Network Classifier.- 18.2.1 Representation of Chromosome Features.- 18.2.2 Network Topology and Training.- 18.2.3 Incorporating Non-Banding Features.- 18.3 Classification Performance.- 18.3.1 Classification Experiments.- 18.3.2 Comparison with Statistical Classifiers.- 18.3.3 The Influence of Training-Set Size.- 18.4 The Use of Context in Classification.- 18.4.1 The Karyotyping Constraint.- 18.4.2 Applying the Constraint by a Network.- 18.4.3 Results of Applying the Context Network.- 18.5 Conclusion and Discussion.- 18.5.1 Comparison with Statistical Classifiers.- 18.5.2 Training Set Size and Application of Context.- 18.5.3 Biological Context.- 19 The Importance of Features and Primitives for Multi-dimensional/Multi-channel Image Processing.- 19.1 Introduction.- 19.2 The Image Data Level.- 19.3 From Image Data to Symbolic Primitives.- 19.4 Region Segmentation Quality and Training Phase.- 19.5 Validation of Image Segmentation.- 19.6 Segmentation Complexity and Quantitative Error Evaluation.- 19.7 Feature Description.- 19.8 Feature Selection.- 19.9 A Preliminary Overview of Application Results.- 19.10 Conclusion.
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