Publications associées à Learning to sample in Cartesian MRI

Publications associées (40)

A Riemannian framework for incorporating white matter bundle prior in orientation distribution function based tractography algorithms

Diffusion magnetic resonance imaging (dMRI) tractography is a powerful approach to study brain structural connectivity. However, its reliability in a clinical context is still highly debated. Recent studies have shown that most classical algorithms achieve ...

2025

Mental State Recognition Deficits Linked to Brain Changes in Parkinson's Disease Without Dementia

Francesca Saviola

Recent studies have reported social cognitive deficits, particularly in emotional processing, in Parkinson's disease (PD). However, a comprehensive characterization of these deficits and their underlying neural correlates remains elusive. Therefore, this s ...

2025

Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction

Meritxell Bach Cuadra, Thomas Sanchez

Background: Fetal brain MRI is increasingly used to complement ultrasound imaging. Images are processed using complex super-resolution reconstruction pipelines, which may bias biometric and volumetric measurements. Objective: To assess the consistency of 2 ...

2025

Fast High-Resolution Metabolite Mapping in the rat Brain Using 1H-FID-MRSI at 14.1 T

Cristina Ramona Cudalbu Moldovan, Bernard Lanz, Dunja Simicic, Jessie Julie Mosso, Thanh Phong Kevin Lê, Guillaume Miro André Briand, Brayan Alves

Magnetic resonance spectroscopic imaging (MRSI) enables the simultaneous noninvasive acquisition of MR spectra from multiple spatial locations inside the brain. Although 1H-MRSI is increasingly used in the human brain, it is not yet widely applied in the p ...

2025

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Zaffar Ahmed Shaikh

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

SAGE PUBLICATIONS LTD2025

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Christel Marie Tartini-Chappuis

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

EPFL2025

IsoBayes: a Bayesian approach for single-isoform proteomics inference

Andrea Cavalli

Motivation Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer th ...

Oxford University Press2025

Probing the latent hierarchical structure of data via diffusion models

Noam Itzhak Levi, Alessandro Favero, Antonio Sclocchi

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

2025

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

In vivo X-ray microtomography locally affects stem radial growth with no immediate physiological impact

Charlotte Grossiord, Pascal Turberg, Laura Mekarni

Microcomputed tomography (mu CT) is a nondestructive X-ray imaging method used in plant physiology to visualize in situ plant tissues that enables assessments of embolized xylem vessels. Whereas evidence for X-ray-induced cellular damage has been reported, ...

Oxford Univ Press Inc2024

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Berend Smit, Sauradeep Majumdar, Xiaoqi Zhang

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Royal Soc Chemistry2024

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Loris Di Natale

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

EPFL2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Amirhossein Shahbazinia, David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Seyedbehzad Bozorgtabar

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Elsevier2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Michele Bianco, Tianyue Chen

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Veronica Lily Ravano

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

EPFL2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024