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Nevertheless, the Fazekas scale's visual assessment exhibits low intra-rater and inter-rater reliability, coupled with a substantial workload. In light of this, we developed a fully automated visual grading system, based upon quantifiable measurements. Our procedure is structured around four steps: (1) deep learning-based segmentation of brain ventricles and WMH lesions; (2) subsequent categorization of these lesions as periventricular white matter hyperin
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