Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Q. | - |
dc.contributor.author | Lee, S.-W. | - |
dc.date.available | 2020-02-27T12:44:05Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4399 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018 | - |
dc.title | Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000454624300039 | - |
dc.identifier.doi | 10.1109/AIKE.2018.00048 | - |
dc.identifier.bibliographicCitation | Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018, pp.208 - 211 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85058234866 | - |
dc.citation.endPage | 211 | - |
dc.citation.startPage | 208 | - |
dc.citation.title | Proceedings - 2018 1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018 | - |
dc.contributor.affiliatedAuthor | Nguyen, Q. | - |
dc.contributor.affiliatedAuthor | Lee, S.-W. | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Colorectal segmentation | - |
dc.subject.keywordAuthor | CRC | - |
dc.subject.keywordAuthor | Encoder-decoder | - |
dc.subject.keywordAuthor | Multi model | - |
dc.subject.keywordPlus | Color image processing | - |
dc.subject.keywordPlus | Decoding | - |
dc.subject.keywordPlus | Diseases | - |
dc.subject.keywordPlus | Endoscopy | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Knowledge engineering | - |
dc.subject.keywordPlus | Augmentation methods | - |
dc.subject.keywordPlus | Colorectal cancer | - |
dc.subject.keywordPlus | Early diagnosis | - |
dc.subject.keywordPlus | Encoder-decoder | - |
dc.subject.keywordPlus | Multi model | - |
dc.subject.keywordPlus | Polyp segmentation | - |
dc.subject.keywordPlus | Size and shape | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Network coding | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scopus | - |
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