DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이문용 | - |
dc.contributor.author | Hong, Sungrae | - |
dc.contributor.author | 홍성래 | - |
dc.date.accessioned | 2024-07-25T19:30:51Z | - |
dc.date.available | 2024-07-25T19:30:51Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045750&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320562 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 데이터사이언스대학원, 2023.8,[iv, 38 p. :] | - |
dc.description.abstract | Medical AI diagnosis including histopathology segmentation has derived benefits from the recent development of deep learning technology. However, deep learning itself requires a large amount of training data and the medical image segmentation masking, in particular, requires an extremely high cost due to the shortage of medical specialists. To mitigate this issue, we propose a new data augmentation method built upon the conventional Copy and Paste (CP) augmentation technique, called CP-Dilatation, and apply it to histopathology image segmentation. To the well-known traditional CP technique, the proposed method adds a dilation operation that can preserve the boundary context information of the malignancy, which is important in histopathological image diagnosis, as the boundary between the malignancy and its margin is mostly unclear and a significant context exists in the margin. To the best of our knowledge, this is the first attempt to treat ground truth masks through Copy-and-Paste augmentation while it is also the first CP-based augmentation study for histopathological image analysis. In our experiments using histopathology benchmark datasets, the proposed method was found superior to the other state-of-the-art baselines chosen for comparison. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 조직병리학▼a이미지 세분화▼a증강▼a복사 및 붙임▼a팽창 연산 | - |
dc.subject | Histopathology▼aImage segmentation▼aAugmentation▼aCopy-and-paste▼aDilatation | - |
dc.title | (A) copy-and-paste augmentation method for preserving the boundary context information of histopathology images | - |
dc.title.alternative | 조직병리학 이미지의 경계 맥락 정보를 보존하는 복제 및 붙임 방식의 증강 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :데이터사이언스대학원, | - |
dc.contributor.alternativeauthor | Yi, Mun Yong | - |
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