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• W4384662334 abstract "Abstract Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of $ 78.98pm 5.86% $ , an F1 score of $ 77.99pm 5.64%, $ and an AUC of $ 0.87pm 0.06 $ . These results set new state-of-the-art benchmarks for this application." @default.
• W4384662334 created "2023-07-20" @default.
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• W4384662334 date "2023-01-01" @default.
• W4384662334 modified "2023-10-17" @default.
• W4384662334 title "Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning" @default.
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• W4384662334 doi "https://doi.org/10.1017/s2633903x23000144" @default.
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