Ntorina Thanou

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Publications associées (39)

Anatomy-informed multimodal learning for myocardial infarction prediction

Pascal Frossard, Emmanuel Abbé, Ortal Yona Senouf, Olivier Muller, Ntorina Thanou, Thabodhan Mahendiran, Ivan-Daniel Sievering, Stéphane Fournier

2022

Interpretable Stability Bounds for Spectral Graph Filters

Ntorina Thanou

Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-structured data, spectral graph filters have been used to solve common tasks such ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

Pascal Frossard, Ntorina Thanou, Effrosyni Simou

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorder

Ntorina Thanou

While the prevalence of Autism Spectrum Disorder (ASD) is increasing, research continues in an effort to identify common etiological and pathophysiological bases. In this regard, modern machine learning and network science pave the way for a better underst ...

ELSEVIER2021

Predicting future myocardial infarction from angiographies with deep learning

Pascal Frossard, Emmanuel Abbé, Ortal Yona Senouf, Olivier Muller, Ntorina Thanou, Thabodhan Mahendiran, Omar Raita, Stéphane Fournier, David Meier, Nicolas Dayer

In patients with stable Coronary Artery Disease (CAD), the identification of lesions which will be responsible of a myocardial infarction (MI) during follow-up remains a daily challenge. In this work, we propose to predict culprit stenosis by applying a de ...

2021

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Pascal Frossard, Ntorina Thanou, Michael Bronstein, Laura Toni, Xiaowen Dong

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Mask Combination of Multi-Layer Graphs for Global Structure Inference

Pascal Frossard, Ntorina Thanou, Elif Vural, Eda Bayram

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although ...

2020

Height And Weight Estimation From Unconstrained Images

Radhakrishna Achanta, Ntorina Thanou

We address the difficult problem of estimating the attributes of weight and height of individuals from pictures taken in completely unconstrained settings. We present a deep learning scheme that relies on simultaneous prediction of human silhouettes and sk ...

IEEE2020

A Graph Signal Processing Framework for the Classification of Temporal Brain Data

Ntorina Thanou, Sarah Itani

Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are co ...

IEEE2020

Graph-based Transform Coding with Application to Image Compression

Pascal Frossard, Ntorina Thanou, Giulia Fracastoro

In this paper, we propose a new graph-based coding framework and illustrate its application to image compression. Our approach relies on the careful design of a graph that optimizes the overall rate-distortion performance through an effective graph-based t ...

2019

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