Publications de Jean-Philippe Thiran

Inverse Problems With Multiple Plane Waves: The Angular Simplification

In unfocused ultrasound imaging, a delay-and-sum algorithm is commonly used to reconstruct one image per emission. When multiple emissions are performed, individual images can be combined by coherent compounding to improve image quality. Alternative method ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Barriers and Facilitators to the Preadoption of a Computer-Aided Diagnosis Tool for Cervical Cancer: Qualitative Study on Health Care Providers' Perspectives in Western Cameroon

Klaus Benedikt Schönenberger, Jean-Philippe Thiran

Background: Computer-aided detection and diagnosis (CAD) systems can enhance the objectivity of visual inspection with acetic acid (VIA), which is widely used in low- and middle-income countries (LMICs) for cervical cancer detection. VIA's reliance on subj ...

JMIR PUBLICATIONS, INC2025

Towards Long-Term Sleep Staging via Wearable Reflective Photoplethysmography

Jean-Philippe Thiran

Study objectives Sleep staging is usually performed by manual scoring of polysomnography (PSG), which is expensive, laborious, and poorly scalable. We propose an alternative to PSG for ambulatory sleep staging using wearable photoplethysmography (PPG) reco ...

Oxford University Press (OUP)2025

Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases

Jean-Philippe Thiran

Recent studies demonstrate promising efficacy with immune checkpoint inhibitors (ICI) for brain metastases (BM), an unmet need in modern oncology. However, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its hig ...

Springer Science and Business Media Deutschland GmbH2025

S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging

Jean-Philippe Thiran, Jean-Paul Richard Kneib, Emma Elizabeth Tolley

Recently, the R2D2 paradigm, standing for ‘Residual-to-Residual DNN series for high-Dynamic-range imaging’, was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R ...

Oxford University Press (OUP)2025

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

Jean-Philippe Thiran, Meritxell Bach Cuadra

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 ...

Springer Science and Business Media Deutschland GmbH2025

A Path-Based Model for Aberration Correction in Ultrasound Imaging

Jean-Philippe Thiran

Pulse-Echo Ultrasound Imaging suffers from several sources of image degradation. In clinical conditions, superficial layers made of different tissues (e.g. skin, fat or muscles) create aberrations that can severely deteriorate image quality. To correct suc ...

2025

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

International Conference on Learning Representations, ICLR2025

A diffusion MRI model for random walks confined on cylindrical surfaces: towards non-invasive quantification of myelin sheath radius

Jean-Philippe Thiran, Elda Fischi Gomez, Erick Jorge Canales Rodriguez

Introduction Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, there are curre ...

FRONTIERS MEDIA SA2025

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel scale of WSIs, conventional multiple ...

IEEE2025

Automation of Wilms’ tumor segmentation by artificial intelligence

Jean-Philippe Thiran

Background: 3D reconstruction of Wilms’ tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate th ...

2024

Sequential CNN-based Enhancement of Ultrafast Ultrasound Imaging for Sparse Arrays

Jean-Philippe Thiran, Roser Vinals Terres

With the emergence of ultra-portable ultrasound systems, the need to reduce cost and complexity of ultrasound systems has intensified. Sparse arrays, employing fewer transducer elements to acquire images, offer a viable solution by effectively minimizing d ...

European Signal Processing Conference, EUSIPCO2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Zenodo2024

Acceptability of artificial intelligence for cervical cancer screening in Dschang, Cameroon: a qualitative study on patient perspectives

Jean-Philippe Thiran

Background: Cervical cancer is the fourth most frequent cancer among women, with 90% of cervical cancer-related deaths occurring in low- and middle-income countries like Cameroon. Visual inspection with acetic acid is often used in low-resource settings to ...

2024

Enhancement of Ultrafast Ultrasound Images: A Performance Comparison Between CNN Trained with RF or IQ Images

Jean-Philippe Thiran, Roser Vinals Terres

Ultrafast ultrasound imaging achieves high frame rates but results in lower image quality compared to focused acquisitions. Convolutional neural networks (CNNs) have emerged as effective tools for reducing diffraction artifacts and enhancing the quality of ...

Institute of Electrical and Electronics Engineers Inc.2024

Spatial Prior Informed Constrained Spherical Deconvolution in Super Resolution Variation

Jean-Philippe Thiran, Gabriel Girard, Erick Jorge Canales Rodriguez, Juan Luis Villarreal Haro

Constrained Spherical Deconvolution (CSD) is a state-of-the-art technique for unraveling complex fiber configurations of white matter from diffusion MRI data. Despite its ability to identify multiple fiber orientations, it faces difficulties in resolving f ...

Springer Nature2024

An Inverse-Problem Approach to the Estimation of Despeckled and Deconvolved Images from Radio-Frequency Signals in Pulse-Echo Ultrasound

Jean-Philippe Thiran, Samuel Denys Beuret

In recent years, there has been notable progress in the development of inverse problems for image reconstruction in pulse-echo ultrasound. Inverse problems are designed to circumvent the restrictions of delay-And-sum, such as limited image resolution and d ...

2024

Susceptibility-induced internal gradients reveal axon morphology and cause anisotropic effects in the diffusion-weighted MRI signal

Jean-Philippe Thiran

Diffusion-weighted MRI is our most promising method for estimating microscopic tissue morphology in vivo. The signal acquisition is based on scanner-generated external magnetic gradients. However, it will also be affected by susceptibility-induced internal ...

Nature Research2024

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Jean-Philippe Thiran, Muhamed Barakovic, Erick Jorge Canales Rodriguez, Marco Pizzolato, Tim Bjørn Dyrby

Purpose: This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approac ...

Wiley2024

Comparing Steam and PGSE Diffusion MRI Signal of RAT Lymph Nodes Using In-Silico Simulations

Jean-Philippe Thiran, Elda Fischi Gomez, Gabriel Girard, Erick Jorge Canales Rodriguez, Rémy Marc M Gardier, Juan Luis Villarreal Haro

Diffusion magnetic resonance imaging (dMRI) has demonstrated great capabilities to quantify tissue microstructure using biophysical diffusion models. Over the years, various gradient sequences have been proposed to provide sensitivity and specificity to va ...

IEEE Computer Society2024