DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation

arXiv:2403.03273v1 Announce Type: cross
Abstract: 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. …

abstract adaptability arxiv availability challenge cs.cv cs.lg datasets deep learning few-shot image medical segmentation solution supervised learning type