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Found 3 papers, 1 papers with code
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
We ensure robustness across sites by training the model on an unprecedented rich dataset aggregating data from open repositories: almost 27, 000 T1w volumes from around 160 acquisition sites, at 1. 5 - 3T, from a population spanning from 8 to 90 years old.
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans.
Deep driven fMRI decoding of visual categories
Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes.
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