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 | 2022-11-25T02:00:15Z | - |
dc.date.available | 2022-11-25T02:00:15Z | - |
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/10203/300939 | - |
dc.description.abstract | Hippocampus segmentation is primarily expected to be precise and robust due to its important role in a timely and accurate diagnosis of brain-related disorders such as Alzheimer's disease. As the previous research using deep learning mostly relied on a large number of labeled data samples, here we investigate the effect of pre-training using the state-of-the-art self-supervised contrastive learning-based technique in order to leverage the performance without large amounts of labeled data. We thus develop a new framework for the task of hippocampus segmentation based on learning local-level discriminative features for better generalization of structural information of MRI brain images. The comparative results of downstream training with different labeled data fractions reveal that pre-training without labels provides a significant margin of improvement. Moreover, we also evaluate and validate the robustness and generalizability through downstream training using a different dataset. | - |
dc.language | English | - |
dc.publisher | CCN | - |
dc.title | Towards Precise and Robust Hippocampus Segmentation using Self-Supervised Contrastive Learning | - |
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.2022.1074-0 | - |
dc.contributor.localauthor | Kim, Dae-Shik | - |
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