DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image
  Segmentation
Zhaoxin Fan Puquan Pan Zeren Zhang Ce Chen Tianyang Wang Siyang Zheng Min Xu
Few-shot medical image semantic segmentation is of paramount importance in
the domain of medical image analysis. However, existing methodologies grapple
with the challenge of data scarcity during the training phase, leading to
over-fitting. To mitigate this issue, we introduce a novel Unsupervised Dense
Few-shot Medical Image Segmentation Model Training Pipeline (DenseMP) that
capitalizes on unsupervised dense pre-training. DenseMP is composed of two
distinct stages: (1) segmentation-aware dense contrastive pre-training, and (2)
few-shot-aware superpixel guided dense pre-training. These stages
collaboratively yield a pre-trained initial model specifically designed for
few-shot medical image segmentation, which can subsequently be fine-tuned on
the target dataset. Our proposed pipeline significantly enhances the
performance of the widely recognized few-shot segmentation model, PA-Net,
achieving state-of-the-art results on the Abd-CT and Abd-MRI datasets. Code
will be released after acceptance.