718 citations
- Harvard UniversityUS102 papers
- Massachusetts Institute of TechnologyUS20 papers
- Massachusetts General HospitalUS17 papers
- University College LondonGB10 papers
- Brigham and Women's HospitalUS9 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS8 papers
- Boston Children's MuseumUS8 papers
- Northeastern UniversityUS7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Loyola University ChicagoUS6 papers
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23 papers · 1 filter
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…
Equivariant Spherical CNNs for Accurate Fiber Orientation Distribution Estimation in Neonatal Diffusion MRI with Reduced Acquisition Time
Haykel Snoussi, Davood Karimi
Early and accurate assessment of brain microstructure using diffusion Magnetic Resonance Imaging (dMRI) is crucial for identifying neurodevelopmental disorders in neonates, but rem…
Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry
Tina Yao, Nicole St. Clair, Gabriel F. Miller +13
Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-…
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski +8
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical ass…
Multimodal Few-Shot Learning with Frozen Language Models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi +3
When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we p…
PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training
Luke Melas-Kyriazi, Arjun K. Manrai
Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming…