3 citations · 12 across the 10 of their papers we have counts for
10 papers
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and models
Pedro M. Gordaliza, Jaume Banus, Benoît Gérin +4
Developing Foundation Models for medical image analysis is essential to overcome the unique challenges of radiological tasks. The first challenges of this kind for 3D brain MRI, SS…
Causal Attribution of Model Performance Gaps in Medical Imaging Under Distribution Shifts
Pedro M. Gordaliza, Nataliia Molchanova, Jaume Banus +2
Deep learning models for medical image segmentation suffer significant performance drops due to distribution shifts, but the causal mechanisms behind these drops remain poorly unde…
Accounting for Underspecification in Statistical Claims of Model Superiority
Thomas Sanchez, Pedro M. Gordaliza, Meritxell Bach Cuadra
Machine learning methods are increasingly applied in medical imaging, yet many reported improvements lack statistical robustness: recent works have highlighted that small but signi…
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
Maxence Wynen, Pedro M. Gordaliza, Maxime Istasse +4
Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely o…
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza +8
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresse…
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
Nataliia Molchanova, Bénédicte Maréchal, Jean-Philippe Thiran +3
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a g…
PhD Thesis. Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning
Pedro M. Gordaliza
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease…
Translational Lung Imaging Analysis Through Disentangled Representations
Pedro M. Gordaliza, Juan José Vaquero, Arrate Muñoz-Barrutia
The development of new treatments often requires clinical trials with translational animal models using (pre)-clinical imaging to characterize inter-species pathological processes.…
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations
Pedro M. Gordaliza, Juan José Vaquero, Sally Sharpe +2
We propose a learning method well-suited to infer the presence of Tuberculosis (TB) manifestations on Computer Tomography (CT) scans mimicking the radiologist reports. Latent featu…