| 한글제목(Korean Title) |
Attentive Transfer Learning via Self-supervised Learning for Cervical Dysplasia Diagnosis |
| 영문제목(English Title) |
Attentive Transfer Learning via Self-supervised Learning for Cervical Dysplasia Diagnosis |
| 저자(Author) |
Jinyeong Chae
Roger Zimmermann
Dongho Kim
Jihie Kim
|
| 원문수록처(Citation) |
VOL 17 NO. 03 PP. 0453 ~ 0461 (2021. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Attention Learning
Cervical Dysplasia
Patch self-supervised Learning
Transfer Learning
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| 파일첨부 |
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