  Deep learning models have emerged as the cornerstone of medical image
segmentation, but their efficacy hinges on the availability of extensive
manually labeled datasets and their adaptability to unforeseen categories
remains a challenge. Few-shot segmentation (FSS) offers a promising solution by
endowing models with the capacity to learn novel classes from limited labeled
examples. A leading method for FSS is ALPNet, which compares features between
the query image and the few available support segmented images. A key question
about using ALPNet is how to design its features. In this work, we delve into
the potential of using features from DINOv2, which is a foundational
self-supervised learning model in computer vision. Leveraging the strengths of
ALPNet and harnessing the feature extraction capabilities of DINOv2, we present
a novel approach to few-shot segmentation that not only enhances performance
but also paves the way for more robust and adaptable medical image analysis.
