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 …

Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything
Model (SAM) serves as a powerful foundation model for visual segmentation and can be …

Computer vision technology for the automatic recognition and geometric characterization of
carbon and glass fibers in scanning electron microscopy images is proposed. The proposed …

The recently introduced Segment-Anything Model (SAM) has the potential to greatly
accelerate the development of segmentation models. However, directly applying SAM to …

Abstract The Segment Anything Model (SAM) can achieve satisfactory segmentation
performance under high-quality box prompts. However, SAM's robustness is compromised …

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

Adapting foundation models for medical image analysis requires finetuning them on a
considerable amount of data because of extreme distribution shifts between natural (source) …

Background Accurate delineation of knee bone boundaries is crucial for computer-aided
diagnosis (CAD) and effective treatment planning in knee diseases. Current methods often …

Segmentation of medical images plays a critical role in various clinical applications, facilitat-
ing precise diagnosis, treatment planning, and disease monitoring. However, the scarcity of …

Computer vision technology for the automatic recognition and geometric characterization of
carbon fibers was proposed in this paper. A two-stage pipeline was used. In the first stage …