DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kunanbayev, Kassymzhomart | ko |
dc.contributor.author | Jang, Donggon | ko |
dc.contributor.author | Lee, Jeongwon | ko |
dc.contributor.author | Kim, Dae-Shik | ko |
dc.date.accessioned | 2022-11-25T01:00:34Z | - |
dc.date.available | 2022-11-25T01:00:34Z | - |
dc.date.created | 2022-11-22 | - |
dc.date.issued | 2022-08-26 | - |
dc.identifier.citation | Conference on Cognitive Computational Neuroscience, CCN 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10203/300937 | - |
dc.description.abstract | Developing automated accurate and robust hippocampus segmentation is associated with the prevention of Alzheimer's disease. In this study, we devise a self-supervised learning framework for hippocampus segmentation while pre-training model without labels and transferring the pre-trained weights for downstream training with fewer labeled data. Results indicate competitive segmentation performance in fewer labeled training, especially in 10% and 20% label fractions, as well as robustness when trained for segmentation on another dataset. | - |
dc.language | English | - |
dc.publisher | CCN | - |
dc.title | Variance-Invariance-Covariance Regularization with Local Self-Supervised Learning Improves Hippocampus Segmentation with Fewer Labels | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | Conference on Cognitive Computational Neuroscience, CCN 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Hyatt Regency San Francisco | - |
dc.identifier.doi | 10.32470/CCN.2022.1075-0 | - |
dc.contributor.localauthor | Kim, Dae-Shik | - |
dc.contributor.nonIdAuthor | Lee, Jeongwon | - |
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