X-ray computed tomography (CT) is a non-destructive technique that offers a 3D insight into
the microstructure of thick (opaque) samples with virtually no preliminary sample …

Prototype learning is extensively used for few-shot segmentation. Typically, a single
prototype is obtained from the support feature by averaging the global object information …

Brain imaging techniques play an important role in determining the causes of brain cell
injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge …

We present CLUSTSEG, a general, transformer-based framework that tackles different
image segmentation tasks (ie, superpixel, semantic, instance, and panoptic) through a …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

Background Recent advancements in in situ gene expression technologies constitute a new
and rapidly evolving field of transcriptomics. With the recent launch of the 10x Genomics …

In computer vision, superpixels have been widely used as an effective way to reduce the
number of image primitives for subsequent processing. But only a few attempts have been …

Superpixels provide an efficient low/mid-level representation of image data, which greatly
reduces the number of image primitives for subsequent vision tasks. Existing superpixel …

Proteins are fundamental biological entities mediating key roles in cellular function and
disease. This paper introduces a multi-scale graph construction of a protein–HoloProt …

Recent work has shown that label-efficient few-shot learning through self-supervision can
achieve promising medical image segmentation results. However, few-shot segmentation …