By Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak (auth.), Helge Malmgren BA, PhD, MD, Magnus Borga MSc, PhD, Lars Niklasson BSc, MSc, PhD (eds.)
This e-book comprises the complaints of the convention ANNIMAB-l, held 13-16 may well 2000 in Goteborg, Sweden. The convention was once prepared via the Society for man made Neural Networks in medication and Biology (ANNIMAB-S), which used to be validated to advertise study inside a brand new and really cross-disciplinary box. Forty-two contributions have been accredited for presentation; as well as those, S invited papers also are integrated. learn inside medication and biology has frequently been characterized via software of statistical equipment for comparing area particular info. The transforming into curiosity in synthetic Neural Networks has not just brought new equipment for info research, but additionally spread out for improvement of recent types of organic and ecological structures. The ANNIMAB-l convention is targeting a few of the many makes use of of synthetic neural networks with relevance for drugs and biology, in particular: • clinical functions of synthetic neural networks: for higher diagnoses and consequence predictions from scientific and laboratory facts, within the processing of ECG and EEG signs, in scientific picture research, and so forth. greater than 1/2 the contributions deal with such clinically orientated matters. • makes use of of ANNs in biology outdoor scientific drugs: for instance, in versions of ecology and evolution, for info research in molecular biology, and (of path) in types of animal and human frightened platforms and their functions. • Theoretical features: contemporary advancements in studying algorithms, ANNs relating to professional platforms and to conventional statistical techniques, hybrid structures and integrative approaches.
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Extra info for Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000
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The position of a patient on the resulting map could be used to aid orthodontic diagnosis and treatment. The example by Glass & Reddick  demonstrates how an SOFM can be used for image segmentation. An SOFM was trained with pixels from viable tumors and necrotic tissue, as visualized by magnetic resonance images. An MLP was then trained to distinguish between these two types of tissue on the basis of the SOFMs final input-node weights. Consequently, the SOFM-MLP combination characterized the type of tissue represented by a new pixel.
Springer-Verlag, New York, 1995  Bishop CM. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995  Penny WD, Roberts SJ. Neural network predictions with error bars. Imperial College, Neural Systems Research Group, Research Report TR-97-1, 1997  Hasan J. Automatic analysis of sleep recordings: a critical review. Annals-Clin-Res. 1985; 17: 280-287  Breiman L. , Statistics Department, University of California, Berkeley, CA, USA, Tech. Rep. 460,1996  Bishop CM. Mixture Density Networks, Department of Computer Science and Applied Mathematics, Aston University, Birmingham, UK, NCRGI941004, 1994  Neal RM.
Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000 by Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak (auth.), Helge Malmgren BA, PhD, MD, Magnus Borga MSc, PhD, Lars Niklasson BSc, MSc, PhD (eds.)