Jean-Philippe Thiran

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High-resolution 3D MR imaging is necessary for the detailed assessment of focal pathologies, such as cortical lesions. However, high-resolution demands tradeoffs with acceleration and SNR, which is difficult to address with standard machine learning reconstructions due to the infeasibility of collecting large datasets of fully sampled data. Using a dataset of high-resolution (0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients, we show that a self-supervised reconstruction from one scan, r

Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FO ...

We present a method that exploits an information theoretic framework to extract optimal audio features with respect to the video features. A simple measure of mutual information between the resulting audio features and the video ones allows to detect the active speaker among different candidates. The results show that our method is able to exploit the shared speech information contained in audio and video signals to recover their common source.

This paper addresses the issue of automatic classification of the six universal emotional categories (joy, surprise, fear, anger, disgust, sadness) in the case of static images. Appearance parameters are extracted by an active appearance model(AAM) represe ...

In this paper, a fast and effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier extracts faces of any pose from the background. Then more specific classifiers discriminate between different poses. The tree of classifiers is trained by hierarchically sub-sampling the pose space. Finally, Condensation algorithm is used for tracking the fac

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the patient image by an affine and an intensity-based non rigid registration. Based on this rough initialisation, the level set functions corresponding to particular ob

In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functio ...

At the typical spatial resolution of MRI in the human brain, approximately 60–90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. Whil ...

Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sp ...

Different approaches to the moving object detection in multi-object tracking systems use dynamic-based models. In this paper we propose the use of a discrete choice model (DCM) of pedestrian behavior and its application to the problem of the target detection in the particular case of pedestrian tracking. We analyze real scenarios assuming to have a calibrated monocular camera, allowing a unique correspondence between the image plan and the top view reconstruction of the scene. In our approach we first initi

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMR ...

Informative sample selection in active learning (AL) helps a machine learning system attain optimum performance with minimum labeled samples, thus improving human-in-the-loop computer-aided diagnosis systems with limited labeled data. Data augmentation is highly effective for enlarging datasets with less labeled data. Combining informative sample selection and data augmentation should leverage their respective advantages and improve performance of AL systems. We propose a novel approach to combine informati

In the recent years, researchers have investigated several methods of facial expression analysis. Their interest has been to apply their algorithms to sets of images labelled by a restrained number of experts. In order to deal with the generalizability of the proposed methods, databases of numerous facial expressions images have been collected. Less attention has been given to the experts. In this paper we present a web-based survey aiming at collecting the judgment on different facial expressions of a hete

Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow. Supervised deep networks take for granted a large number of annotations by radiologists, which is often prohibitively very ti ...

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of n ...

In magnetic resonance imaging, the application of a strong diffusion weighting suppresses the signal contributions from the less diffusion-restricted constituents of the brain's white matter, thus enabling the estimation of the transverse relaxation time T ...

Motivation: Unlock the full potential of ultra-high field MRI enabling the detection of subtle structural changes. Goal(s): Investigate the performance of a Deep Learning (DL)-based brain segmentation algorithm for 7T MR Images. Approach: We trained a DL model for volume-based morphometry using T1-weighted brain images acquired at 7T. We then evaluated model performance on 7T scans for 80 epilepsy patients scanned at both 7T and 3T. Segmentations and volumetric estimates obtained from the patients' scans at

Validation is arguably the bottleneck in the diffusion MRI community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction chal ...

In this paper, we propose to estimate soil moisture in bare soils directly from hyperspectral imagery using support vector regression (nu-SVR). nu-SVR is a supervised non-parametric learning technique, e.g. making no assumption on the underlying data distr ...

Jean-Philippe Thiran

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Self-Supervised Image Reconstruction of 7T MP2RAGE for Multiple Sclerosis: 0.5mm Isotropic Resolution in 10 Minutes

Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains

A multimodal approach to extract optimized audio features for speaker detection

Fisher's Discriminant and Relevant Component Analysis for static facial expression classification

Fast Multiview Face Tracking with Pose Estimation

Atlas-based segmentation of medical images locally constrained by level sets

Atlas-based segmentation of medical images locally constrained by level sets

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA) - II. Code and real data proof of concept

Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation

USSR: An UltraSound Sparse Regularization framework

A discrete choice pedestrian behavior model for pedestrian detection in visual tracking systems

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Combining Graph Transformers Based Multi-Label Active Learning and Informative Data Augmentation for Chest Xray Classification

A Web Survey for Facial Expressions Evaluation

Self-Taught Semi-Supervised Anomaly Detection On Upper Limb X-Rays

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

Advancing Brain Morphometry at 7T: A Pilot Study on Epilepsy Patients

Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI

Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression