Publications by 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

Jean-Philippe Thiran, Klaus Benedikt Schönenberger

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, Seyedbehzad Bozorgtabar, Thomas Grégoire Stegmüller

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, Erick Jorge Canales Rodriguez, Elda Fischi Gomez

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, Devavrat Tomar, Seyedbehzad Bozorgtabar

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