Persistence diagrams are one of the main tools in the field of Topological Data Analysis
(TDA). They contain fruitful information about the shape of data. The use of machine learning …

We present a topological method for the detection and quantification of bone microstructure
from non-linear microscopy images. Specifically, we analyse second harmonic generation …

Skin cancer is one of the most common cancers in the United States. As technological
advancements are made, algorithmic diagnosis of skin lesions is becoming more important …

The application of artificial intelligence (AI) to various medical subfields has been a popular
topic of research in recent years. In particular, deep learning has been widely used and has …

Structural disorder can improve the optical properties of metasurfaces, whether it is
emerging from some large-scale fabrication methods or explicitly designed and built …

This work is dedicated to the topological analysis of complex transitional networks for
dynamic state detection. Transitional networks are formed from time series data and they …

Topological data analysis (TDA) is a rising field in the intersection of mathematics, statistics,
and computer science/data science. Persistent homology is one of the most commonly used …

This paper accompanies Team Cordi-Aks entry to the classification of 12-lead ECGs for the
PhysioNet/Computing in Cardiology Challenge 2020. Our approach leverages …

Topological Data Analysis (TDA) is a rising field of computational topology in which the
topological structure of a data set can be observed by persistent homology. By considering a …

Copy number changes play an important role in the development of cancer and are
commonly associated with changes in gene expression. Persistence curves, such as Betti …