Semi-supervised learning (SSL) has garnered considerable attention in medical image segmentation due to its ability to leverage abundant unlabeled data, thereby significantly alleviating the ...

Abstract: Existing cross-modality unsupervised domain adaptation methods for medical image segmentation typically rely on style transfer techniques to mitigate unintentional domain gaps, while they ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Every generation views the rise of technology in photography differently. For our great-grandparents, even early cameras that produced dull, grainy photos felt revolutionary. Today, that contrast is ...

Fundus vessel segmentation is crucial for the early diagnosis of ocular diseases. However, existing deep learning-based methods, although effective for detecting coarse vessels, still face challenges ...

Abstract: Image super resolution focuses on increasing the spatial resolution of low-quality images and enhancing their visual quality. Since the image degradation process is unknown in real-life ...