A semi-supervised framework that decomposes T1 and FA MRI features into shared and sequence-specific parts, then uses reliable pseudo-labels from unlabeled data, improves visual pathway segmentation accuracy.
Diffusivity and quantitative t1 profile of human visual white matter tracts after retinal ganglion cell damage,
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Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation
A semi-supervised framework that decomposes T1 and FA MRI features into shared and sequence-specific parts, then uses reliable pseudo-labels from unlabeled data, improves visual pathway segmentation accuracy.