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Artificial Neural Networks in Biomedicine 2000 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 288 pages, kõrgus x laius: 235x155 mm, kaal: 950 g, 30 Illustrations, black and white; XIV, 288 p. 30 illus., 1 Paperback / softback
  • Sari: Perspectives in Neural Computing
  • Ilmumisaeg: 02-Feb-2000
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852330058
  • ISBN-13: 9781852330057
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Artificial Neural Networks in Biomedicine 2000 ed.Suurem pilt
  • Formaat: Paperback / softback, 288 pages, kõrgus x laius: 235x155 mm, kaal: 950 g, 30 Illustrations, black and white; XIV, 288 p. 30 illus., 1 Paperback / softback
  • Sari: Perspectives in Neural Computing
  • Ilmumisaeg: 02-Feb-2000
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852330058
  • ISBN-13: 9781852330057
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781852330057.html
Märksõnad:
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.

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Springer Book Archives
Introduction 1(8)
TUTORIAL AND REVIEW 9(40)
The Bayesian Paradigm: Second Generation Neural Computing
11(14)
Introduction
11(1)
Theory
12(8)
Bayesian Learning
13(2)
The Evidence Framework
15(1)
Error bars
16(1)
Moderated outputs
16(1)
Regularisation
17(1)
Committees
18(2)
Example Results
20(1)
Conclusion
21(4)
The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease
25(14)
Introduction
26(2)
Diagnosis of Disease
28(2)
Outcome Prediction
30(1)
Conclusion
31(8)
Genetic Evolution of Neural Network Architectures
39(10)
Introduction
39(1)
Stability: The `Bias/Variance Problem'
40(1)
Genetic Algorithms and Artificial Neural Networks
41(5)
Description of a General Method for Evolving ANN Architecture (EANN)
42(1)
Prediction of Depression After Mania
43(1)
EANN and the Agreement/Transparency Choice
43(2)
ANN and the Stability/Specialisation Choice
45(1)
Conclusion
46(3)
COMPUTER AIDED DIAGNOSIS 49(102)
The Application of PAPNET to Diagnostic Cytology
51(18)
Introduction
51(1)
First Efforts at Automation in Cytology
52(1)
Neural Networks
53(1)
The PAPNET System®
53(10)
Components of the PAPNET System
54(5)
Technical factors affecting the performance of the machine
59(1)
Performance of the PAPNET System
59(1)
Cervicovaginal smears
59(2)
Application of the PAPNET System to Smears of Sputum
61(1)
Application of the PAPNET System to Smears of Urinary Sediment
61(1)
Application of the PAPNET System to Oesophageal Smears
62(1)
Comment
63(6)
ProstAsure Index -- A Serum-Based Neural Network-Derived Composite Index for Early Detection of Prostate Cancer
69(12)
Introduction
69(1)
Clinical Background of Prostate Cancer and Derivation of the ProstAsure Index Algorithm
70(2)
Validation of PI with Independent Clinical Data
72(1)
Issues in Developing PI
73(3)
Conclusion
76(5)
Neurometric Assessment of Adequacy of Intraoperative Anaesthetic
81(12)
Intraoperative Awareness
81(1)
Measuring Sensory Perception
82(1)
Clinical Data
82(1)
Results
83(3)
Implementation
86(2)
Clinical Deployment
88(1)
Healthcare Benefit
89(1)
Additional Studies
89(4)
Classifying Spinal Measurements Using a Radial Basis Function Network
93(12)
Introduction
93(1)
Data
94(2)
The Spines
94(1)
The Measurements
94(1)
Preprocessing the Data
95(1)
Radial Basis Functions and Networks
96(1)
Matrix Notation
97(1)
Training RBF Networks
98(4)
The Unsupervised Learning Stage
98(1)
The Supervised Learning Stage
98(1)
Regularisation as an aid to avoid over-fitting
98(1)
Calculating the regularisation coefficients and the weights
99(2)
Forward subset selection of RBFs
101(1)
Input feature selection
102(1)
Results
102(1)
Conclusion
103(2)
GEORGIA: An Overview
105(12)
Introduction
106(1)
The Medical Decision Support System
107(2)
Learning Pattern Generation
109(1)
Software and Hardware Implementation
110(1)
Re-Training and Re-Configuring the MDSS
111(1)
Introducing GEORGIA's Man-to-Computer Interface
111(3)
Conclusion
114(3)
Patient Monitoring Using an Artificial Neural Network
117(12)
Overview of the Medical Context
117(1)
Basic Statistical Appraisal of Vital Function Data
118(2)
Neural Network Details
120(3)
Default Training
121(2)
Implementation
123(1)
Clinical Trials
124(1)
Clinical Practice
125(4)
Benchmark of Approaches to Sequential Diagnosis
129(12)
Introduction
129(1)
Preliminaries
130(2)
Methods
132(5)
The Probabilistic Algorithm
132(1)
The diagnostic algorithm for first order markov chains- the Markov I algorithm
132(1)
The diagnostic algorithm for second order markov chains-the Markov II algorithm
133(2)
The Fuzzy Methods
135(1)
The algorithm without context - fuzzy 0
135(1)
The algorithm with first-order context - fuzzy 1A
135(1)
The reduced algorithm with first-order context - fuzzy 1B
135(1)
The algorithm with second-order context - fuzzy 2A
135(1)
The reduced algorithm with second-order context - fuzzy 2B
136(1)
The Neural Network Approach
136(1)
A Practical Example - Comparative Analysis of Methods
137(1)
Conclusion
138(3)
Application of Neural Networks in the Diagnosis of Pathological Speech
141(10)
Introduction
141(1)
The Research Material and the Problems Considered
142(3)
Dental Prosthetics
142(1)
Maxillofacial Surgery
143(1)
Orthodontics
144(1)
Laryngology
144(1)
The Signal Parameterisation
145(2)
The Application of the Neural Networks and the Results
147(2)
Conclusion
149(2)
SIGNAL PROCESSING 151(60)
Independent Components Analysis
153(16)
Introduction
153(1)
Theory
153(5)
The Decorrelating Manifold
155(1)
The Choice of Non-Linearity
156(2)
Model-Order Estimation
158(1)
Non-Stationary ICA
158(5)
Illustration
161(2)
Applications
163(3)
Source Separation
164(1)
Source Number and Estimation
164(2)
Conclusion
166(3)
Rest EEG Hidden Dynamics as a Discriminant for Brain Tumour Classification
169(12)
Introduction
170(1)
Characterising Hidden Dynamics
171(3)
The Clinical Study
174(2)
The Minimum Markov Order
176(3)
Conclusion
179(2)
Artifical Neural Network Control on Functional Electrical Stimulation Assisted Gait for Persons with Spinal Cord Injury
181(14)
Introduction
182(1)
Methods
183(4)
Results
187(4)
Discussion
191(4)
The Application of Neural Networks to Interpret Evoked Potential Waveforms
195(16)
Introduction
195(1)
The Medical Conditions Studied
196(1)
The Evoked Potentials
196(1)
The Relationship Between the CNV and the Medical Conditions
197(1)
Experimental Procedures
198(1)
Data Pre-Processing
198(1)
Feature Extraction
199(1)
Normalisation
200(1)
The Artificial Neural Networks
200(6)
The Simplified Fuzzy ARTMAP
200(4)
The Probabilistic Simplified Fuzzy ARTMAP
204(1)
ANN Training and Accuracy
205(1)
Small numbers of training vectors
205(1)
Simplified fuzzy ARTMAP
205(1)
Committees of ANNs
206(1)
Validation Issues
206(1)
Technical Aspects of Validation
206(1)
Clinical Aspects of Validation
206(1)
Results
207(1)
Implementation Considerations
207(1)
Future Developments
208(3)
IMAGE PROCESSING 211(72)
Intelligent Decision Support Systems in the Cytodiagnosis of Breast Carcinoma
213(20)
Introduction
213(2)
Previous Work on Decision Support in this Domain
215(1)
The Data Set in this Study
215(11)
Study Population
215(1)
Input Variables
216(1)
Partitioning of the Data
217(1)
Human Performance
217(1)
Logistic Regression
218(1)
Data Derived Decision Tree
219(1)
Multi-Layer Perceptron Neural Networks
220(2)
Adaptive Resonance Theory Mapping (ARTMAP) Neural Networks
222(1)
Potential Advantages of ARTMAP
222(1)
ARTMAP Architecture and Methodology
222(3)
Results from the Cascaded System
225(1)
Symbolic Rule Extraction
225(1)
Assessment of the Different Decision Support Systems
226(7)
A Neural-Based System for the Automatic Classification and Follow-Up of Diabetic Retinopathies
233(16)
Introduction
233(2)
The DRA System
235(2)
Hybrid Module
237(2)
Committee Algorithms
239(6)
New Selection Algorithms
240(1)
Greedy selection
241(1)
Pseudo-exhaustive selection
241(1)
Sequential Cooperation
242(1)
Experimental Results
243(2)
Related Work
245(1)
Validation of the DRA System
245(1)
Conclusion
246(3)
Classification of Chromosomes: A Comparative Study of Neural Network and Statistical Approaches
249(18)
Introduction
249(3)
Chromosome Analysis and its Applications
249(1)
Chromosome Classification
250(1)
Experimental Data
251(1)
The Neural Network Classifier
252(3)
Representation of Chromosome Features
252(1)
Network Topology and Training
253(1)
Incorporating Non-Banding Features
254(1)
Classification Performance
255(3)
Classification Experiments
255(1)
Comparison with Statistical Classifiers
256(1)
The Influence of Training-Set Size
256(2)
The Use of Context in Classification
258(3)
The Karyotyping Constraint
258(1)
Applying the Constraint by a Network
259(1)
Results of Applying the Context Network
260(1)
Conclusion and Discussion
261(6)
Comparison with Statistical Classifiers
261(1)
Training Set Size and Application of Context
262(1)
Biological Context
263(4)
The Importance of Features and Primitives for Multi-dimensional/Multi-channel Image Processing
267(16)
Introduction
267(2)
The Image Data Level
269(1)
From Image Data to Symbolic Primitives
269(1)
Region Segmentation Quality and Training Phase
270(1)
Validation of Image Segmentation
271(2)
Segmentation Complexity and Quantitative Error Evaluation
273(2)
Feature Description
275(1)
Feature Selection
276(2)
A Preliminary Overview of Application Results
278(3)
Conclusion
281(2)
Index 283