DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.09604v1
• Date: Thu, 13 Jul 2023 15:18:15 GMT
• Title: DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
• Authors: Zhaoxin Fan, Puquan Pan, Zeren Zhang, Ce Chen, Tianyang Wang, Siyang Zheng, Min Xu
• Abstract summary: Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis. We introduce a novel Unsupervised Few-shot Medical Image Model Training Pipeline (DenseMP) that capitalizes on unsupervised dense pre-training. Our proposed pipeline significantly enhances the performance of the widely recognized few-shot segmentation model, PA-Net.
• Score: 6.51140268845611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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