Abstract
In this paper, we present an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate similarity transformation which is based on a self-organized deformation of the underlying multidimensional probability distributions. After discussing the theory of the DM algorithm, we present results of its application to the real-world problem of fully automatic voxel-based multispectral image segmentation, employing magnetic resonance data sets of the human brain.
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D.R. Dersch. Eigenschaften neuronaler Vektorquantisierer und ihre Anwendung in der Sprachverarbeitung. Verlag Harri Deutsch, Reihe Physik, Bd. 54, Thun, Frankfurt am Main, 1996. ISBN 3-8171-1492-3.
F. Girosi and T. Poggio. Networks and the best approximation property. Biological Cybernetics, 63: 169–176, 1990.
J. Kohlmorgen, K.R. Müller, and K. Pawelzik. Improving short-term prediction with competing experts. In Proceedings of the International Conference on Artificial Neural Networks ICANN, volume 2, pages 215–220, Paris, 1995. EC2 & Cie.
T. Kohonen. Self-Organization and Associative Memory. Springer, Berlin, 1989.
J. Moody and C. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281–294, 1989.
K. Rose, E. Gurewitz, and G.C. Fox. Vector quantization by deterministic annealing. IEEE Transactions on Information Theory, 38(4):1249–1257, 1992.
D.E. Rumelhart and J.L. McClelland. Learning internal representations by error propagation. In Parallel Distributed Processing, volume I. M.I.T. Press, Cambridge, MA, 1986.
J. Walter and H. Ritter. Investment learning with hierarchical PSOM. In A. Wismüller and D.R. Dersch, editors, Symposion über biologische Informationsverarbeitung und Neuronale Netze - SINN ’95, Konferenzband, pages 161–168. Hanns-Seidel-Stiftung, München, 1996.
A. Wismüller and D.R. Dersch. Neural network computation in biomedical research: chances for conceptual cross-fertilization. Theory in Biosciences, 116(3), 1997.
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Wismüller, A. et al. (2000). Elastic Distortion of Deformable Feature Maps for Fully-Automatic Segmentation of Multispectral MRI Data Sets of the Human Brain. In: Horsch, A., Lehmann, T. (eds) Bildverarbeitung für die Medizin 2000. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59757-2_22
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DOI: https://doi.org/10.1007/978-3-642-59757-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67123-7
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