Nuclear instance segmentation has been critical for pathology image analysis in medical
science, eg, cancer diagnosis. Current methods typically adopt pixel-wise optimization for …

Segmentation of curvilinear structures such as vasculature and road networks is challenging
due to relatively weak signals and complex geometry/topology. To facilitate and accelerate …

Semi-supervised crowd counting is an important yet challenging task. A popular approach is
to iteratively generate pseudo-labels for unlabeled data and add them to the training set …

In digital pathology, segmenting densely distributed objects like glands and nuclei is crucial
for downstream analysis. Since detailed pixel-wise annotations are very time-consuming, we …

Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant
clinical implications across various medical applications. However, existing methods often …

In computational pathology, segmenting densely distributed objects like glands and nuclei is
crucial for downstream analysis. To alleviate the burden of obtaining pixel-wise annotations …

Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides
critical insights into tissue structure and pathology. The synthetic generation of cell topology …

In computer vision, keypoint detection is a fundamental task, with applications spanning from
robotics to image retrieval; however, existing learning-based methods suffer from scale …

Microvascular anatomy is known to be involved in various neurological disorders. However,
understanding these disorders is hindered by the lack of imaging modalities capable of …

Neuronal anatomy is central to the organization and function of brain cell types. However,
anatomical variability within apparently homogeneous populations of cells can obscure such …