Attentive Transfer Learning via Self-supervisedLearning for Cervical Dysplasia Diagnosis


Jinyeong Chae, Roger Zimmermann, Dongho Kim, Jihie Kim, Journal of Information Processing Systems Vol. 17, No. 3, pp. 453-461, Jun. 2021  

https://doi.org/10.3745/JIPS.04.0214
Keywords: Attention Learning, Cervical Dysplasia, Patch self-supervised Learning, Transfer Learning
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Abstract

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch selfsupervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity."


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Cite this article
[APA Style]
Chae, J., Zimmermann, R., Kim, D., & Kim, J. (2021). Attentive Transfer Learning via Self-supervisedLearning for Cervical Dysplasia Diagnosis. Journal of Information Processing Systems, 17(3), 453-461. DOI: 10.3745/JIPS.04.0214.

[IEEE Style]
J. Chae, R. Zimmermann, D. Kim, J. Kim, "Attentive Transfer Learning via Self-supervisedLearning for Cervical Dysplasia Diagnosis," Journal of Information Processing Systems, vol. 17, no. 3, pp. 453-461, 2021. DOI: 10.3745/JIPS.04.0214.

[ACM Style]
Jinyeong Chae, Roger Zimmermann, Dongho Kim, and Jihie Kim. 2021. Attentive Transfer Learning via Self-supervisedLearning for Cervical Dysplasia Diagnosis. Journal of Information Processing Systems, 17, 3, (2021), 453-461. DOI: 10.3745/JIPS.04.0214.