The last decade saw an enormous boost in the field of computational topology: methods and
concepts from algebraic and differential topology, formerly confined to the realm of pure …

Topological Data Analysis (TDA) is a recent and fast growing field providing a set of new
topological and geometric tools to infer relevant features for possibly complex data. This …

Examination of chest X-ray images is currently one of the most important methods for the
screening and diagnosis of thoracic diseases and, in some cases, for assessing response to …

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 …

Deep Learning based segmentation models for medical imaging often fail under subtle
distribution shifts calling into question the robustness of these models. Medical images …

Topological invariants of a dataset, such as the number of holes that survive from one length
scale to another (persistent Betti numbers) can be used to analyse and classify data in …

Fine-grained 3D segmentation is an important task in 3D object understanding, especially in
applications such as intelligent manufacturing or parts analysis for 3D objects. However …

The computer vision task of reconstructing 3D images, ie, shapes, from their single 2D
image slices is extremely challenging, more so in the regime of limited data. Deep learning …

In this article, we study Euler characteristic techniques in topological data analysis.
Pointwise computing the Euler characteristic of a family of simplicial complexes built from …

Reconstructing 3D objects from 2D images is both challenging for our brains and machine
learning algorithms. To support this spatial reasoning task, contextual information about the …