Transformers for medical image segmentation have attracted broad interest. Unlike
convolutional networks (CNNs), transformers use self-attentions that do not have a strong …

Recent advancements in biomedical image analysis have been significantly driven by the
Segment Anything Model (SAM). This transformative technology, originally developed for …

Stroke remains a significant global health concern, necessitating precise and efficient
diagnostic tools for timely intervention and improved patient outcomes. The emergence of …

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside
the skull or brain, which can be attributed to various factors. Identifying, localizing and …

Segment Anything Model (SAM), a vision foundation model trained on large-scale
annotations, has recently continued raising awareness within medical image segmentation …

Intracranial haemorrhage (ICH) has become a critical healthcare emergency that needs
accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%) …

Intracranial hemorrhage (ICH) is a common and critical disease in clinical, with rapid
progression, high disability, and mortality rates. Existing segmentation methods, such as U …

Intracranial hemorrhage (ICH) is a common head disease that can result in significant
disability or mortality. Segmentation of ICH is an important yet challenging step for medical …

Intracranial hemorrhage poses a critical threat to patient survival, necessitating rapid
intervention to prevent devastating outcomes. Traditional segmentation methods in …

Segment anything model (SAM), a foundation model with superior versatility and
generalization across diverse segmentation tasks, has attracted widespread attention in …