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Proceedings Paper

Adaptive-sized hybrid neural network for segmentation of breast cancer cells in pathology images
Author(s): Akira Hasegawa; Kevin J. Cullen; Seong Ki Mun
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Paper Abstract

In this report, we describe a novel method to automatically segment several kinds of cells in breast cancer pathology images. The information on the number of cells is expected to assist pathologists in consistent diagnosis of breast cancer. Currently, most pathologists make a diagnosis based on a rough estimation of the number of cells on an image. Because of the rough estimation, the diagnosis is not objective. To assist pathologists to make a consistent, objective and fast diagnosis, it is necessary to develop a computer system to automatically recognize and count several kinds of cells. As the first step for this purpose, we propose a novel neural network model, called an adaptive-sized hybrid neural network (ASH-NN), and develop a method based on this network model to segment cells from breast cancer pathology images. The proposed neural network consists of three layers and the connection weights between the first and second layers are updated by self-organization, and the weights between the second and third layers are determined based on supervised learning. The ASH-NN has the capability of (1) automatic adjustment of the number of hidden units and (2) quick learning.

Paper Details

Date Published: 19 February 1996
PDF: 7 pages
Proc. SPIE 2645, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, (19 February 1996); doi: 10.1117/12.233062
Show Author Affiliations
Akira Hasegawa, Georgetown Univ. Medical Ctr. (United States)
Kevin J. Cullen, Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2645:
24th AIPR Workshop on Tools and Techniques for Modeling and Simulation
Donald J. Gerson, Editor(s)

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