An unsupervised deep-clustering vocabulary of liver MRI patches separated NASH treatment groups better than fat fraction and ALT and predicted biopsy grades, with replication in a second cohort.
Deep learning enables pathologist-like scoring of NASH models,
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Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
An unsupervised deep-clustering vocabulary of liver MRI patches separated NASH treatment groups better than fat fraction and ALT and predicted biopsy grades, with replication in a second cohort.