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Proceedings Paper

PAPNET TM: an automated cytology screener using image processing and neural networks
Author(s): Randall L. Luck; Robert Tjon-Fo-Sang; Laurie Mango; Joel R. Recht; Eunice Lin; James Knapp
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Paper Abstract

The Pap smear is the universally accepted test used for cervical cancer screening. In the United States alone, about 50 to 70 million of these test are done annually. Every one of the tests is done manually be a cytotechnologist looking at cells on a glass slide under a microscope. This paper describes PAPNET, an automated microscope system that combines a high speed image processor and a neural network processor. The image processor performs an algorithmic primary screen of each image. The neural network performs a non-algorithmic secondary classification of candidate cells. The final output of the system is not a diagnosis. Rather it is a display screen of suspicious cells from which a decision about the status of the case can be made.

Paper Details

Date Published: 1 April 1992
PDF: 11 pages
Proc. SPIE 1623, The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges, (1 April 1992); doi: 10.1117/12.58066
Show Author Affiliations
Randall L. Luck, Aspex, Inc. (United States)
Robert Tjon-Fo-Sang, Neuromedical Systems, Inc. (United States)
Laurie Mango, Neuromedical Systems, Inc. and Montefiore Medical Ctr. (United States)
Joel R. Recht, Neuromedical Systems, Inc. (United States)
Eunice Lin, Montefiore Medical Ctr. (United States)
James Knapp, Aspex, Inc. (United States)

Published in SPIE Proceedings Vol. 1623:
The 20th AIPR Workshop: Computer Vision Applications: Meeting the Challenges

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