A Review on Few-shot Learning for Medical Image Segmentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Y. | - |
dc.contributor.author | Kang, D. | - |
dc.contributor.author | Mok, Y. | - |
dc.contributor.author | Kwon, S. | - |
dc.contributor.author | Paik, Joon Ki | - |
dc.date.accessioned | 2023-09-15T02:49:27Z | - |
dc.date.available | 2023-09-15T02:49:27Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67638 | - |
dc.description.abstract | Deep-learning based approach has solved various medical imaging problems successfully. Since the lack of training data issues caused by patient privacy, the few-shot learning method has been studied widely. However, this issue still afflicts model performance even in few-shot learning methods. To solve this issue, it is important to quickly optimize the initial parameter values using a small amount of data. In addition, to utilize small data effectively, it is important to design the objective function for segmentation suitable for GT (Ground Truth) with few-shots. In this paper, we experiment with various algorithms using the MAML (Model Agnostic Meta-Learning) method. And we propose an optimal few-shot semantic segmentation network. The proposed method uses a gradient descent algorithm and optimizer parameter decomposition method to ensure fast convergence with fewer data. Experimental results show high performance and fast convergence using fewer datasets than conventional methods. © 2023 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A Review on Few-shot Learning for Medical Image Segmentation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICEIC57457.2023.10049899 | - |
dc.identifier.bibliographicCitation | 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85150448780 | - |
dc.citation.title | 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | few-shot learning | - |
dc.subject.keywordAuthor | medical image | - |
dc.subject.keywordAuthor | Meta-learning | - |
dc.subject.keywordAuthor | MRI | - |
dc.subject.keywordAuthor | semantic segmentation | - |
dc.description.journalRegisteredClass | scopus | - |
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