Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images
- Authors
- Nguyen, Q.; Lee, S.-W.
- Issue Date
- Sep-2018
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Colorectal segmentation; CRC; Encoder-decoder; Multi model
- Citation
- Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018, pp.208 - 211
- Journal Title
- Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018
- Start Page
- 208
- End Page
- 211
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4399
- DOI
- 10.1109/AIKE.2018.00048
- ISSN
- 0000-0000
- Abstract
- Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder-decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi-model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB database shows that our proposed method outperforms state-of-the-art results. © 2018 IEEE.
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