Self-Supervised Learning for 3D Patch-Based Medical Image Segmentation
Oct 1, 2024
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1 min read
A self-supervised contrastive learning framework for 3D medical image segmentation using overlap-aware patch sampling.
The framework improves representation learning by explicitly modeling spatial overlap between 3D patches, leading to stronger downstream segmentation performance with limited annotations.
