Filtering normal papanicolaou smear with multi-instance learning

Filtering normal papanicolaou smear using computer-aided system can help clinical doctors to detect cervical cancer. In this paper, we propose a scheme to classify cervical cells as normal or abnormal. The pipeline includes preprocessing, perinuclear area extraction, feature extraction and multi-ins...

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Bibliographic Details

Published in:	2016 IEEE International Conference on Signal and Image Processing (ICSIP) pp. 113 - 117
Main Authors:	Jie Wang, Xun Liu, Yunjie Chen, Yuan Liu, Lei Pan, Huijuan Zhang, Xiang Ji, Su Zhang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2016
Subjects:	Cervical cancer Classification algorithms Feature extraction Manuals multi-instance learning(MIL) papanicolaou smear Pathology Pipelines stacked sparse autoencoder(SSAE) textural features
Online Access:	Get full text
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