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Abstract The increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective histopathological assessment. Conventional histological approaches are time-consuming and subjective. Therefore, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features of MASH/MASLD.
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