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Found 4 papers, 1 papers with code
Automatic 8-tissue Segmentation for 6-month Infant Brains
Accurate brain tissue segmentations for morphological analysis are essential in numerous infant studies.
Applications of interpretable deep learning in neuroimaging: a comprehensive review
We also reviewed five properties of iDL explanations that were analysed in the included studies: biological validity, robustness, continuity, selectivity, and downstream task performance.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.
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