Publications de Ntorina Thanou

AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

Emmanuel Abbé, Ntorina Thanou, Ortal Yona Senouf, Edward Carlo Giorgio Andò, Thabodhan Mahendiran

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional c ...

Elsevier BV2024

Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Pascal Frossard, Ntorina Thanou

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in ...

Springer International Publishing Ag2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Ivan-Daniel Sievering, Ortal Yona Senouf

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

2024

Graph Signal Separation Based on Smoothness or Sparsity in the Frequency Domain

Ntorina Thanou

In this paper, we study the problem of demixing an observed signal, which is the summation of a set of signals that live on a multi-layer graph, by proposing several methods to decompose the observed signal into structured components. For this purpose, we ...

2023

Towards AI-assisted cardiology: a reflection on the performance and limitations of using large language models in clinical decision-making

Emmanuel Abbé, Ntorina Thanou, Mehdi Ali Gadiri

Europa Edition2023

Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Omar Raita, Ortal Yona Senouf, Thabodhan Mahendiran

BackgroundAngiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).AIMSWe compared the ability of humans, angiographic parameters and deep learning (DL ...

BMJ PUBLISHING GROUP2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Nicola Parolini, Ntorina Thanou, David Kraus, Andrea Kraus, Daniel Lopez, Markus Scholz, Benjamin Bejar Haro, Daniel Rodrigo Lopez, Ekaterina Krymova, Tao Sun

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Ntorina Thanou

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT

Pascal Frossard, Emmanuel Abbé, Stéphane Fournier, Ntorina Thanou, David Meier, Jakub Jan Gwizdala, Ortal Yona Senouf, Olivier Muller

2022

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

Ntorina Thanou, Benjamin Bejar Haro, Christine Choirat, Guillaume Romain Obozinski, Ekaterina Krymova

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated ...

National Academy of Sciences2022

Anatomy-informed multimodal learning for myocardial infarction prediction

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

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, Stéphane Fournier, Ntorina Thanou, David Meier, Nicolas Dayer, Farhang Aminfar, Omar Raita, Ortal Yona Senouf, Thabodhan Mahendiran, Olivier Muller

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, Xiaowen Dong, Laura Toni, Michael Bronstein

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