Jean-Philippe Thiran

Full Professor

École Polytechnique Fédérale de Lausanne

Switzerland

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About

Jean-Philippe Thiran is a Full Professor at the Ecole Polytechnique Fédérale de Lausanne in Switzerland. His research areas include inverse problems in imaging, diffusion-weighted MRI, and self-supervised learning in computational pathology. Recent articles authored by him focus on topics such as susceptibility-induced gradients in MRI, robust speed-of-sound imaging, and abnormal cardiac rhythm detection using photoplethysmography signals.

Articles (20)

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2025

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2022

Collaborators (6)

Maximilian Jonas Wessel

GERMANY

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Marco Pizzolato

Technical University of Denmark

DENMARK

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Andéol Cadic-Melchior

EPFL (École Polytechnique Fédérale de Lausanne)

SWITZERLAND

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Tim Bjørn Dyrby

Technical University of Denmark

DENMARK

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Daniel Riccio

University of Naples Federico II

ITALY

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Jonas Richiardi

Lausanne University Hospital

SWITZERLAND

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Jean-Philippe Thiran

Research Interests

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About

Articles (20)

Inverse Problems With Multiple Plane Waves: The Angular Simplification

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

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

Enhancement of time-dependent coherent backscattering: Leveraging phase information

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Windowed Radon Transform for Robust Speed-of-Sound Imaging With Pulse-Echo Ultrasound

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound

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

Abnormal Cardiac Rhythm Detection Based on Photoplethysmography Signals and a Recurrent Neural Network

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity.

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

Insights from the IronTract challenge

Data-driven myelin water imaging based on T1 and T2 relaxometry

CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

Hierarchical graph representations in digital pathology

Impact of scanner variability on lymph node segmentation in computational pathology

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection

Collaborators (6)

Maximilian Jonas Wessel

Marco Pizzolato

Andéol Cadic-Melchior

Tim Bjørn Dyrby

Daniel Riccio

Jonas Richiardi

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