63 citations · 291 across the 50 of their papers we have counts for
8 papers · 1 filter
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…
Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Marina Grifell i Plana, Vladyslav Zalevskyi, Léa Schmidt +6
Accurate fetal brain segmentation is crucial for extracting biomarkers and assessing neurodevelopment, especially in conditions such as corpus callosum dysgenesis (CCD), which can…
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +9
T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
Nataliia Molchanova, Vatsal Raina, Andrey Malinin +7
This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of white matter lesion (WML) segment…
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection
Tommaso Di Noto, Meritxell Bach Cuadra, Chirine Atat +5
Gliomas are the most frequent primary brain tumors in adults. Glioma change detection aims at finding the relevant parts of the image that change over time. Although Deep Learning…
MBIS: Multivariate Bayesian Image Segmentation Tool
Oscar Esteban, Gert Wollny, Subrahmanyam Gorthi +4
We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias…