Polyp segmentation with consistency training and continuous update of pseudo-label
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Hyun-Cheol | - |
dc.contributor.author | Poudel, Sahadev | - |
dc.contributor.author | Ghimire, Raman | - |
dc.contributor.author | Lee, Sang-Woong | - |
dc.date.accessioned | 2022-10-12T06:40:12Z | - |
dc.date.available | 2022-10-12T06:40:12Z | - |
dc.date.created | 2022-09-22 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85658 | - |
dc.description.abstract | Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model's exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset's essential information to improve performance. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.title | Polyp segmentation with consistency training and continuous update of pseudo-label | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000846571700059 | - |
dc.identifier.doi | 10.1038/s41598-022-17843-3 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.12, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85137091045 | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Park, Hyun-Cheol | - |
dc.contributor.affiliatedAuthor | Poudel, Sahadev | - |
dc.contributor.affiliatedAuthor | Ghimire, Raman | - |
dc.contributor.affiliatedAuthor | Lee, Sang-Woong | - |
dc.type.docType | Article | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.