Current Result Document :
| 한글제목(Korean Title) |
비지도학습의 딥 컨벌루셔널 자동 인코더를 이용한 셀 이미지 분류 |
| 영문제목(English Title) |
Cell Images Classification using Deep Convolutional Autoencoder of Unsupervised Learning |
| 저자(Author) |
칼렙
박진혁
권오준
이석환
권기룡
Caleb Vununu
Jin-Hyeok Park
Oh-Jun Kwon
Suk-Hwan Lee
Ki-Ryong Kwon
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| 원문수록처(Citation) |
VOL 28 NO. 02 PP. 0942 ~ 0943 (2021. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.
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| 키워드(Keyword) |
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| 파일첨부 |
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