Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL may complement …

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 …

Accurate segmentation of long and thin tubular structures is required in a wide variety of
areas such as biology, medicine, and remote sensing. The complex topology and geometry …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Vascular segmentation in medical imaging plays a crucial role in analysing morphological
and functional assessments. Traditional methods, like the centerline Dice (clDice) loss …

We propose a new topological tool for computer vision-Scalar Function Topology
Divergence (SFTD), which measures the dissimilarity of multi-scale topology between …

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

While conventional computer vision emphasizes pixel-level and feature-based objectives,
medical image analysis of intricate biological structures necessitates explicit representation …

Cerebrovascular segmentation is a crucial preliminary task for many computer-aided
diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning …