Adaptive-sized hybrid neural network for segmentation of breast cancer cells in pathology images

Akira Hasegawa; Kevin J. Cullen M.D.; Seong Ki Mun

doi:10.1117/12.233062

19 February 1996 Adaptive-sized hybrid neural network for segmentation of breast cancer cells in pathology images

Akira Hasegawa, Kevin J. Cullen M.D., Seong Ki Mun

Proceedings Volume 2645, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation; (1996) https://doi.org/10.1117/12.233062
Event: 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, 1995, Washington, DC, United States

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.

Citation Download Citation

Akira Hasegawa, Kevin J. Cullen M.D., and Seong Ki Mun "Adaptive-sized hybrid neural network for segmentation of breast cancer cells in pathology images", Proc. SPIE 2645, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, (19 February 1996); https://doi.org/10.1117/12.233062

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available