Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology

In this paper, a Stacked Sparse Autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Sof...

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Bibliographic Details

Published in:	Proceedings (International Symposium on Biomedical Imaging) pp. 999 - 1002
Main Authors:	Jun Xu, Lei Xiang, Renlong Hang, Jianzhong Wu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2014
Subjects:	Breast cancer Breast Cancer Histopathology Decoding Deep learning Neural networks Principal component analysis Sparse Autoencoder Testing Training
ISSN:	1945-7928
Online Access:	Get full text
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