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
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.
