76 citations · 115 across the 13 of their papers we have counts for
14 papers
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
Veronica Marsico, Antonio Quintero-Rincon, Hadj Batatia
This study presents a novel method for diagnosing respiratory diseases using image data. It combines Epanechnikov's non-parametric kernel density estimation (EKDE) with a bimodal l…
Hybrid Pipeline SWD Detection in Long-Term EEG Signals
Antonio Quintero Rincon, Nicolas Masino, Veronica Marsico +1
Spike-and-wave discharges (SWDs) are the electroencephalographic hallmark of absence epilepsy, yet their manual identification in multi-day recordings remains labour-intensive and…
Innovative tokenisation of structured data for LLM training
Kayvan Karim, Hani Ragab Hassen. Hadj Batatia
Data representation remains a fundamental challenge in machine learning, particularly when adapting sequence-based architectures like Transformers and Large Language Models (LLMs)…
Integrating External Tools with Large Language Models to Improve Accuracy
Nripesh Niketan, Hadj Batatia
This paper deals with improving querying large language models (LLMs). It is well-known that without relevant contextual information, LLMs can provide poor quality responses or ten…
Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution
Antonio Quintero-Rincón, Carlos D'Giano, Hadj Batatia
This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is prop…
A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures
Antonio Quintero-Rincon, Carlos D'Giano, Hadj Batatia
The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a mo…
A novel spike-and-wave automatic detection in EEG signals
Antonio Quintero-Rincón, Valeria Muro, Carlos D'Giano +2
Spike-and-wave discharge (SWD) pattern classification in electroencephalography (EEG) signals is a key problem in signal processing. It is particularly important to develop a SWD a…
Statistical modeling and classification of reflectance confocal microscopy images
Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel +2
This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. The aim is to identify and characterize the lentigo, a phenom…
An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images
Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel +2
This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification…
EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
M/EEG source localization is an open research issue. To solve it, it is important to have good knowledge of several physical parameters to build a reliable head operator. Amongst t…
Bayesian Structured Sparsity Priors for EEG Source Localization Technical Report
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
This report introduces a new hierarchical Bayesian model for the EEG source localization problem. This model promotes structured sparsity to search for focal brain activity. This s…
A Hamiltonian Monte Carlo Method for Non-Smooth Energy Sampling
Lotfi Chaari, Jean-Yves Tourneret, Caroline Chaux +1
Efficient sampling from high-dimensional distributions is a challenging issue which is encountered in many large data recovery problems involving Markov chain Monte Carlo schemes.…
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia +1
This paper addresses the problem of estimating the Potts parameter B jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Sta…
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
Marcelo Pereyra, Nicolas Dobigeon, Hadj Batatia +1
This report considers the problem of computing the Cramer-Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of i…