The segment anything model (SAM) was released as a foundation model for image
segmentation. The promptable segmentation model was trained by over 1 billion masks on …

One of the complex challenges faced presently by tissue engineering (TE) is the
development of vascularized constructs that accurately mimic the extracellular matrix (ECM) …

Deep learning models have demonstrated remarkable success in multi-organ segmentation
but typically require large-scale datasets with all organs of interest annotated. However …

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image
segmentation to limited computing resources. There is tremendous value in developing …

The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has
raised significant public concern. Accurately identifying tissue alteration regions is crucial for …

In digital pathology, the traditional method for deep learning-based image segmentation
typically involves a two-stage process: initially segmenting high-resolution whole slide …

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics
treatment evaluation and clinical research. The complex kidney system comprises various …

Lupus nephritis (LN) is the most common symptom of systemic lupus erythematosus,
emphasizing its importance in the field of medicine. The growing frequency of LN has …

Current whole slide image (WSI) segmentation aims at extracting tumor regions from the
background. Unlike this, segmenting distinct tumor areas (instances) within a WSI driven by …

Universal segmentation models offer significant potential in addressing a wide range of
tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities …