State Space Model (SSM) is a mathematical model used to describe and analyze the
behavior of dynamic systems. This model has witnessed numerous applications in several …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Convolutional Neural Networks (CNNs) and Transformers have been the most popular
architectures for biomedical image segmentation, but both of them have limited ability to …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Accurate medical image segmentation demands the integration of multi-scale information,
spanning from local features to global dependencies. However, it is challenging for existing …

Generalist methods for cellular segmentation have good out-of-the-box performance on a
variety of image types; however, existing methods struggle for images that are degraded by …

Accurate segmentation of objects in microscopy images remains a bottleneck for many
researchers despite the number of tools developed for this purpose. Here, we present …

Recent advances in segmentation foundation models have enabled accurate and efficient
segmentation across a wide range of natural images and videos, but their utility to medical …

Advancements in imaging technologies have revolutionized our ability to deeply profile
pathological tissue architectures, generating large volumes of imaging data with …

Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited
generality and often fall short when compared to specialized models. Recently, LLM-based …