DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
This work addresses the challenge of adapting deep learning models to unforeseen categories in medical imaging with limited data, though it appears incremental as it builds on existing methods.
The paper tackled the problem of few-shot medical image segmentation by integrating DINOv2 features into the ALPNet framework, resulting in enhanced performance for learning novel classes from limited labeled examples.
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.