Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …

Volume-wise labeling in 3D medical images is a time-consuming task that requires
expertise. As a result, there is growing interest in using semi-supervised learning (SSL) …

Acquiring pixel-level annotations is often limited in applications such as histology studies
that require domain expertise. Various semi-supervised learning approaches have been …

Semi-supervised learning has garnered significant interest as a method to alleviate the
burden of data annotation. Recently, semi-supervised medical image segmentation has …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Abstract The Segment Anything Model (SAM) has garnered significant attention for its
versatile segmentation abilities and intuitive prompt-based interface. However its application …

Image segmentation achieves significant improvements with deep neural networks at the
premise of a large scale of labeled training data, which is laborious to assure in medical …

The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects
in natural images, serving as a versatile perceptual tool for various downstream image …