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Proceedings Paper

Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer
Author(s): Chris Yuzheng Wu; Shih-Chung Benedict Lo; Matthew T. Freedman; Akira Hasegawa; Rebecca A. Zuurbier; Seong Ki Mun
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Paper Abstract

A convolution neural network (CNN) was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen. The input signals to the CNN were the pixel values of image blocks centered on each of the suspected microcalcifications. The CNN has been shown to be capable of recognizing different image patterns. Digital images were acquired by digitizing radiographs at a high resolution of 21 micrometers X 21 micrometers . Eighty regions of interest (ROIs) selected from digitized radiographs of pathological specimen were used for the training and testing of the neural network system. The performance of the neural network system was analyzed using the ROC analysis.

Paper Details

Date Published: 11 May 1994
PDF: 12 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175099
Show Author Affiliations
Chris Yuzheng Wu, Georgetown Univ. Medical Ctr. (United States)
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Akira Hasegawa, Osaka Univ. (United States)
Rebecca A. Zuurbier, Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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