TY - GEN
T1 - Transfer learning based classification of cervical cancer immunohistochemistry images
AU - Li, C.
AU - Xue, D.
AU - Zhou, X.
AU - Zhang, J.
AU - Zhang, H.
AU - Yao, Y.
AU - Kong, F.
AU - Zhang, L.
AU - Sun, H.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/24
Y1 - 2019/8/24
N2 - Cervical cancer is the fourth leading cause of cancer-related deaths. It is very important to make the precise diagnosis for the early stage of cervical cancer. In recent years, transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in cervical histopathology image classification becomes a new research domain. In this paper, we propose a transfer learning framework of Inception-V3 network to classify well, moderately and poorly differentiated cervical histopathology images, which are stained using immunohistochemistry methods. In this framework, an Inception-V3 based transfer learning structure is first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from the structure. Finally, the extracted features are designed for the final classification. In the experiment, a practical images stained by AQP, HIF and VEGF approaches are applied to test the proposed transfer learning network, and an average accuracy of 77.3% is finally achieved.
AB - Cervical cancer is the fourth leading cause of cancer-related deaths. It is very important to make the precise diagnosis for the early stage of cervical cancer. In recent years, transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in cervical histopathology image classification becomes a new research domain. In this paper, we propose a transfer learning framework of Inception-V3 network to classify well, moderately and poorly differentiated cervical histopathology images, which are stained using immunohistochemistry methods. In this framework, an Inception-V3 based transfer learning structure is first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from the structure. Finally, the extracted features are designed for the final classification. In the experiment, a practical images stained by AQP, HIF and VEGF approaches are applied to test the proposed transfer learning network, and an average accuracy of 77.3% is finally achieved.
KW - Cervical cancer
KW - Classification
KW - Histopathology image
KW - Immunohistochemistry staining
KW - Inception-V3
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85077564304&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077564304&partnerID=8YFLogxK
U2 - 10.1145/3364836.3364857
DO - 10.1145/3364836.3364857
M3 - Conference contribution
AN - SCOPUS:85077564304
T3 - ACM International Conference Proceeding Series
SP - 102
EP - 106
BT - ISICDM 2019 - Conference Proceedings
T2 - 3rd International Symposium on Image Computing and Digital Medicine, ISICDM 2019
Y2 - 24 August 2019 through 26 August 2019
ER -