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Jean-Philippe Thiran

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Related publications (496)

Improving Graph-Based Tractography Plausibility Using Microstructure Information

Jean-Philippe Thiran, Alessandro Daducci, Muhamed Barakovic, Gabriel Girard

2019

A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI

Jean-Philippe Thiran, Meritxell Bach Cuadra, Alessandro Daducci, Alessandro Mella

In this paper we tackle the problem of regularisation for inverse problems in single shell diffusion weighted image restoration. Our aim is to recover a high-resolution and denoised DWI signal, prior to any model fitting. The main contribution of our metho ...

SPRINGER-VERLAG BERLIN2019

DermoNet: densely linked convolutional neural network for efficient skin lesion segmentation

Jean-Philippe Thiran, Saleh Baghersalimi, Hazim Kemal Ekenel

Recent state-of-the-art methods for skin lesion segmentation are based on convolutional neural networks (CNNs). Even though these CNN-based segmentation approaches are accurate, they are computationally expensive. In this paper, we address this problem and ...

2019

Robust T2 Relaxometry with Hamiltonian MCMC for Myelin Water Fraction Estimation

Jean-Philippe Thiran, Marco Pizzolato, Thomas Yu

We present a voxel-wise Bayesian multi-compartment T2 relaxometry fitting method based on Hamiltonian Markov Chain Monte Carlo (HMCMC) sampling. The T 2 spectrum is modeled as a mixture of truncated Gaussian components, which involves the estimation of par ...

IEEE2019

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

Jean-Philippe Thiran, Meritxell Bach Cuadra, Cristina Granziera, Mario Joao Fartaria de Oliveira, Po-Jui Lu, Francesco La Rosa

London, SAGE PUBLICATIONS LTD2019

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Hazim Kemal Ekenel

By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart. Although such objective functions generate near-ph ...

IEEE COMPUTER SOC2019

Thalamotomy for tremor normalizes aberrant pre-therapeutic visual cortex functional connectivity

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Elena Najdenovska, Thomas William Arthur Bolton, Constantin Tuleasca

OXFORD UNIV PRESS2019

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Jean-Philippe Thiran, Mohammad Saeed Rad, Seyedbehzad Bozorgtabar

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

IEEE COMPUTER SOC2019

Fourier dimensionality reduction for fast radio transients

Jean-Philippe Thiran, Yves Wiaux, Vijay Kartik, Ming Jiang

In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose ...

2019

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

Jean-Philippe Thiran, Meritxell Bach Cuadra, Ahmed Abdulkadir, Cristina Granziera, Mario Joao Fartaria de Oliveira, Po-Jui Lu, Francesco La Rosa

SAGE PUBLICATIONS LTD2019

G2-VER: Geometry Guided Model Ensemble for Video-based Facial Expression Recognition

Jean-Philippe Thiran, Saleh Baghersalimi, Tanguy Albrici, Guillaume Marc Georges Vray, Hazim Kemal Ekenel

This paper addresses the problem of automatic facial expression recognition in videos, where the goal is to predict discrete emotion labels best describing the emotions expressed in short video clips. Building on a pre-trained convolutional neural network ...

IEEE2019

On Problem Formulation, Efficient Modeling and Deep Neural Networks for High-Quality Ultrasound Imaging

Adrien Besson, Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

Recently, many pulse-echo ultrasound (US) imaging methods have relied on the transmission of unfocused wavefronts. Such a strategy allows for very high frame rates at the cost of a degraded image quality. In this work, we present a regularized inverse prob ...

IEEE2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Jonathan Rafael Patino Lopez, Erick Jorge Canales Rodriguez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Diversity of Cortico-descending Projections: Histological and Diffusion MRI Characterization in the Monkey

Jean-Philippe Thiran, Graham Knott, Giorgio Innocenti, Maxime Descoteaux, Tim Bjørn Dyrby

The axonal composition of cortical projections originating in premotor, supplementary motor (SMA), primary motor (a4), somatosensory and parietal areas and descending towards the brain stem and spinal cord was characterized in the monkey with histological ...

OXFORD UNIV PRESS INC2019

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

IEEE Institute of Electrical and Electronics Engineers2019

Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging

Jean-Philippe Thiran, Giovanni De Micheli, Federico Angiolini, Aya Mohamed Naguib Ahmed Ibrahim, Marcel Arditi, Shuping Zhang

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are ...

2018

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

Jonas Richiardi, Jean-Philippe Thiran, Meritxell Bach Cuadra, Tobias Kober, Cristina Granziera, Mario Joao Fartaria de Oliveira, Francesco La Rosa

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease pat ...

Springer2018

Pulse-Stream Models In Time-Of-Flight Imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson

This paper considers the problem of reconstructing raw signals from random projections in the context of time-of-flight imaging with an array of sensors. It presents a new signal model, coined as multi-channel pulse-stream model, which exploits pulse-strea ...

IEEE2018

Multimodal microstructure imaging: joint T2-relaxometry and diffusometry to estimate myelin, intracellular, extracellular, and cerebrospinal fluid properties

Jean-Philippe Thiran, Alessandro Daducci, Erick Jorge Canales Rodriguez, Marco Pizzolato

We propose a multimodal joint estimation that aims at exploiting the complementary information of diffusion and multi-echo spin echo data to disentangle the contributions and properties of the main tissue microstructure compartments. We recovered T2, diffu ...

ISMRM2018

Accelerated T-2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI

Jean-Philippe Thiran, Reto Meuli, Tobias Kober, Tom Hilbert

2018

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