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Found 4 papers, 3 papers with code
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data.
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities.
FetMRQC: Automated Quality Control for fetal brain MRI
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies.
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.
