Publications by Xavier Bresson

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Jean-Philippe Thiran, Pierre Vandergheynst, Alessandra Griffa, Kirell Maël Benzi, Patric Hagmann, Alessandro Daducci, Xavier Bresson, Benjamin Ricaud

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural ...

Elsevier2017

Consistency of Cheeger and Ratio Graph Cuts

Xavier Bresson

This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective fu ...

Microtome Publishing2016

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Pierre Vandergheynst, Xavier Bresson, Rodrigo Cerqueira Gonzalez Pena

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot ...

Ieee2016

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or wor ...

2016

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

Pierre Vandergheynst, Kirell Maël Benzi, Xavier Bresson, Vasilis Kalofolias

This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode ...

Ieee2016

Robust Principal Component Analysis on Graphs

Pierre Vandergheynst, Xavier Bresson, Michael Bronstein, Nauman Shahid, Vasilis Kalofolias

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first iss ...

2015

Mapping resting-state dynamics on spatio-temporal graphs: a combined functional and diffusion MRI approach

Jean-Philippe Thiran, Pierre Vandergheynst, Alessandra Griffa, Kirell Maël Benzi, Xavier Bresson, Patric Hagmann, Benjamin Ricaud

2015

Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

Meritxell Bach Cuadra, Xavier Bresson, Anca-Ramona Ciurte

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to ...

Institute of Electrical and Electronics Engineers2014

Harmonic Active Contours

Jean-Philippe Thiran, Xavier Bresson, Dominique Zosso, Virginia Estellers Casas

We propose a segmentation method based on the geometric representation of images as two-dimensional manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set ...

Institute of Electrical and Electronics Engineers2014

Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

Jean-Philippe Thiran, Meritxell Bach Cuadra, Olivier Cuisenaire, Xavier Bresson, Nawal Houhou, Anca-Ramona Ciurte

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the ...

Public Library Science2014