https://scholars.lib.ntu.edu.tw/handle/123456789/636596
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Lo, Kuang Cheng | en_US |
dc.contributor.author | Lin, Cheng Wei | en_US |
dc.contributor.author | Lee, Hsin Ying | en_US |
dc.contributor.author | Hsu, Hao | en_US |
dc.contributor.author | WINSTON HSU | en_US |
dc.contributor.author | TUNG-HUNG SU | en_US |
dc.contributor.author | Chen, Shih Yu | en_US |
dc.contributor.author | YUNG-MING JENG | en_US |
dc.date.accessioned | 2023-10-26T05:27:44Z | - |
dc.date.available | 2023-10-26T05:27:44Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.isbn | 9781665473583 | - |
dc.identifier.issn | 19457928 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/636596 | - |
dc.description.abstract | Cellular segmentation is a fundamental prerequisite to many biological analyses. With the development of multiplexed imaging technologies, the need for accurately segmenting individual cells has significantly increased in recent years. However, current deep learning methods cannot deal with staining markers in an arbitrary order or different numbers. Moreover, acquiring pixel-level annotation is incredibly time-consuming in high-dimensional images. To tackle these issues, we incorporate pathology knowledge into our model and present a novel self-training framework. Concretely, we apply a serial attention mechanism and pooling operation to compress the multi-channel image during the training process. Afterward, the nuclei information guides the self-training in the pseudo-label stage. Experiments demonstrate our method is superior to the existing methods in both qualitative and quantitative results. | en_US |
dc.relation.ispartof | Proceedings - International Symposium on Biomedical Imaging | en_US |
dc.subject | Cell segmentation | CODEX | deep learning | highly-multiplexed imaging | self-training | en_US |
dc.title | Self-Training with High-Dimensional Markers for Cell Instance Segmentation | en_US |
dc.type | conference paper | en_US |
dc.identifier.doi | 10.1109/ISBI53787.2023.10230837 | - |
dc.identifier.scopus | 2-s2.0-85172114707 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85172114707 | - |
dc.relation.journalvolume | 2023-April | en_US |
dc.relation.pageend | 5 | en_US |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.cerifentitytype | Publications | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | MediaTek-NTU Research Center | - |
crisitem.author.dept | Internal Medicine | - |
crisitem.author.dept | Internal Medicine-NTUH | - |
crisitem.author.dept | Pathology | - |
crisitem.author.dept | Pathology-NTUH | - |
crisitem.author.orcid | 0000-0002-3330-0638 | - |
crisitem.author.orcid | 0000-0002-6747-7941 | - |
crisitem.author.orcid | 0000-0002-3878-4491 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
顯示於: | 資訊工程學系 |
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