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

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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.
Publisher
CCN
Issue Date
2022-08-26
Language
English
Citation

Conference on Cognitive Computational Neuroscience, CCN 2022

DOI
10.32470/CCN.2022.1075-0
URI
http://hdl.handle.net/10203/300937
Appears in Collection
EE-Conference Papers(학술회의논문)
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