Self-supervised Learning of Dense Hierarchical Representations for
  Medical Image Segmentation
Eytan Kats Jochen G. Hirsch Mattias P. Heinrich
This paper demonstrates a self-supervised framework for learning voxel-wise
coarse-to-fine representations tailored for dense downstream tasks. Our
approach stems from the observation that existing methods for hierarchical
representation learning tend to prioritize global features over local features
due to inherent architectural bias. To address this challenge, we devise a
training strategy that balances the contributions of features from multiple
scales, ensuring that the learned representations capture both coarse and
fine-grained details. Our strategy incorporates 3-fold improvements: (1) local
data augmentations, (2) a hierarchically balanced architecture, and (3) a
hybrid contrastive-restorative loss function. We evaluate our method on CT and
MRI data and demonstrate that our new approach particularly beneficial for
fine-tuning with limited annotated data and consistently outperforms the
baseline counterpart in linear evaluation settings.