P-1.26

Variance-Invariance-Covariance Regularization with Local Self-Supervised Learning Improves Hippocampus Segmentation with Fewer Labels

Kassymzhomart Kunanbayev, Donggon Jang, Jeongwon Lee, Dae-Shik Kim, KAIST, Korea (South)

Session:
Posters 1 Poster

Track:
Cognitive science

Location:
Pacific Ballroom H-O

Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -8)

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.

Manuscript:
License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1075-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
Presentation
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Session P-1
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