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Proceedings Paper

Neural network compression for medical images: the dynamic autoassociative neural net compression system
Author(s): Andres Rios; Mansur R. Kabuka
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Paper Abstract

This paper discusses the use of a novel model of neural networks, the generalized neural network model, to build the primitives for an adaptive compression system. This model adds to the today's connectionist model paradigms to include the behave-act, evolve-learn, and behave-control functions of neural networks, which allow the definition of connectionist systems that overcome the drawbacks of previous feedforward neural network-based compression systems. The approach yields a compression system that surpasses known compression algorithms in three main aspects: very high compression rate with a low introduced distortion, ability to tackle a broad set of data, and feasibility for on-line real-time compression.

Paper Details

Date Published: 1 May 1994
PDF: 12 pages
Proc. SPIE 2164, Medical Imaging 1994: Image Capture, Formatting, and Display, (1 May 1994); doi: 10.1117/12.174007
Show Author Affiliations
Andres Rios, Univ. of Miami (United States)
Mansur R. Kabuka, Univ. of Miami (United States)


Published in SPIE Proceedings Vol. 2164:
Medical Imaging 1994: Image Capture, Formatting, and Display
Yongmin Kim, Editor(s)

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