Download Artificial Neural Networks in Medicine and Biology: by Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, PDF

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 publication comprises the complaints of the convention ANNIMAB-l, held 13-16 may possibly 2000 in Goteborg, Sweden. The convention used to be prepared via the Society for synthetic Neural Networks in drugs and Biology (ANNIMAB-S), which used to be tested to advertise learn inside of a brand new and really cross-disciplinary box. Forty-two contributions have been permitted for presentation; as well as those, S invited papers also are integrated. study inside medication and biology has frequently been characterized by way of software of statistical equipment for comparing area particular info. The starting to be curiosity in man made Neural Networks has not just brought new tools for information research, but in addition spread out for improvement of latest versions of organic and ecological platforms. The ANNIMAB-l convention is concentrating on many of the many makes use of of man-made neural networks with relevance for drugs and biology, particularly: • scientific purposes of man-made neural networks: for larger diagnoses and final result predictions from medical and laboratory info, within the processing of ECG and EEG indications, in clinical snapshot research, and so on. greater than 1/2 the contributions handle such clinically orientated matters. • makes use of of ANNs in biology outdoors scientific drugs: for instance, in versions of ecology and evolution, for facts research in molecular biology, and (of direction) in versions of animal and human worried structures and their services. • Theoretical elements: fresh advancements in studying algorithms, ANNs in terms of professional platforms and to conventional statistical strategies, hybrid platforms and integrative approaches.

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British Journal of Urology, 80 Supplement 3:53-8, 1997. P. S. T. Freedman. Update in digital mammography. Critical Reviews in Diagnostic Imaging, 38(1):89-113, 1997. Y. Ktonas. Computer-based recognition of EEG patterns. Electroencephalography £3 Clinical Neurophysiology - Supplement, 45:23-35, 1996. [10] W. Penny and D. Frost. Neural networks in clinical medicine. Medical Decision Making, 16(4):386-389, 1996. [11] D. B. J. J. Oetgen. Artificial neural networks: Current status in cardiovascular medicine.

With regard to the first example, an SOFM was used to extract the most relevant information from mandibular growth data. The position of a patient on the resulting map could be used to aid orthodontic diagnosis and treatment. The example by Glass & Reddick [36] 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.

Classification of low back pain from dynamic motion characteristics using an artificial neural network. Spine, 22(24):2991-2998, 1997. 36 [46] R Dybowski. Classification of incomplete feature vectors by radial basis function networks. Pattern Recognition Letters, 19:1257-1264, 1998. [47] LT. Nabney. Efficient training of RBF networks for classification. Technical report NCRG/99/002, Neural Computing Research Group, Aston University, 1999. A. Carpenter and S. Grossberg. A massively parallel architecture for a selforganizing neural pattern recognition machine.

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