Unsupervised Medical

Unsupervised medical image analysis aims to extract meaningful information from medical images without relying on manually labeled data, addressing the limitations of data scarcity and annotation costs. Current research focuses on developing unsupervised segmentation techniques using graph neural networks and autoencoders augmented with contrastive learning, as well as adapting models across different medical image domains using meta-learning approaches. These advancements improve the efficiency of feature extraction, image reconstruction, and clustering, enabling applications such as patient stratification, improved diagnostic tools, and data augmentation for downstream tasks. The ultimate goal is to unlock the potential of large, unlabeled medical image datasets for improved clinical decision-making and personalized medicine.