The lack of annotated medical images limits the performance of deep learning models,
which usually need large-scale labelled datasets. Few-shot learning techniques can reduce …

Automated segmentation is a fundamental medical image analysis task, which enjoys
significant advances due to the advent of deep learning. While foundation models have …

Precision medicine, such as patient-adaptive treatments utilizing medical images, poses
new challenges for image segmentation algorithms due to (1) the large variability across …

The application of large-scale models in medical image segmentation demands substantial
quantities of meticulously annotated data curated by experts along with high computational …

Accurate segmentation of polyp regions in gastrointestinal endoscopic images is pivotal for
diagnosis and treatment. Despite advancements, challenges persist, like accurately …

The utilization of robust, pre-trained foundation models enables simple adaptation to specific
ongoing tasks. In particular, the recently developed Segment Anything Model (SAM) has …

Change detection is a critical task in remote sensing image analysis, widely used in fields
such as land cover change and urban planning. With the introduction of foundational models …

The Segment Anything Model (SAM) has demonstrated strong performance in image
segmentation of natural scene images. However, its effectiveness diminishes markedly …

Under the backdrop of large-scale pre-training, large visual models (LVM) have
demonstrated significant potential in image understanding. The recent emergence of the …