Publications by Emmanuel Abbé

Boosted decoders for reed-muller codes

Various embodiments are directed to Reed-Muller decoding systems and methods configured to decode RM codes by processing each received noisy codeword f of the received RM encoded data to determine therefrom a respective decoding that best fits the noisy co ...

2025

Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography

Emmanuel Abbé

Purpose: This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for invasive coronary angiography (ICA) in high-risk non-ST-elevation (NSTE) acute coronary syndrome (ACS) patients based on bot ...

2024

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

Gender Bias in AI's Perception of Cardiovascular Risk

Emmanuel Abbé

The study investigated gender bias in GPT-4's assessment of coronary artery disease risk by presenting identical clinical vignettes of men and women with and without psychiatric comorbidities. Results suggest that psychiatric conditions may influence GPT-4 ...

2024

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

A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis

Emmanuel Abbé

BACKGROUND: Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients' data can be hindered by logistical challenges, which in turn may affect the care they recei ...

EUROPA EDITION2024

Can Knowledge Transfer Techniques Compensate for the Limited Myocardial Infarction Data by Leveraging Haemodynamics? An in silico Study

Emmanuel Abbé, Simone Deparis, Annalisa Buffa, Riccardo Tenderini, Ortal Yona Senouf

The goal of this work is to investigate the ability of transfer learning (TL) and multitask learning (MTL) algorithms to predict tasks related to myocardial infarction (MI) in a small-data regime, leveraging a larger dataset of haemodynamic targets. The da ...

Springer Nature2023

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

ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?

Emmanuel Abbé, Stéphane Fournier, Thabodhan Mahendiran, Olivier Muller

Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correct ...

Oxford Univ Press2023

Reed-Muller Codes

Emmanuel Abbé

Reed-Muller (RM) codes are among the oldest, simplest and perhaps most ubiquitous family of codes. They are used in many areas of coding theory in both electrical engineering and computer science. Yet, many of their important properties are still under inv ...

NOW PUBLISHERS INC2023

ChatGPT takes on the european exam in core cardiology: an AI success story

Emmanuel Abbé, Stéphane Fournier, Olivier Muller

Oxford Univ Press2023

Polynomial-time universality and limitations of deep learning

Emmanuel Abbé

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

WILEY2023

Learning Sparse Graphons And The Generalized Kesten-Stigum Threshold

Emmanuel Abbé

The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the re-cent years in sparser regimes. Yet, the current techniques still require diverg-ing degrees in order to succe ...

INST MATHEMATICAL STATISTICS-IMS2023

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

A proof that Reed-Muller codes achieve Shannon capacity on symmetric channels

Emmanuel Abbé, Colin Peter Sandon

In 1948, Shannon used a probabilistic argument to show that there exist codes achieving a maximal rate defined by the channel capacity. In 1954, Muller and Reed introduced a simple deterministic code construction, conjectured shortly after to achieve chann ...

Ieee Computer Soc2023

An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn

Emmanuel Abbé, Elisabetta Cornacchia, Jan Hazla

This paper introduces the notion of "Initial Alignment" (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent on a fu ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

AN l(p) THEORY OF PCA AND SPECTRAL CLUSTERING

Emmanuel Abbé

Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning. While existing study of PCA focuses on the recovery of principal components and their associated eigenvalues, there are few precise characterizations of individual pr ...

INST MATHEMATICAL STATISTICS-IMS2022

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

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Emmanuel Abbé

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

ASSOC COMPUTING MACHINERY2022

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Emmanuel Abbé

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

IEEE COMPUTER SOC2022