Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning - Télécom Paris Accéder directement au contenu
Article Dans Une Revue Biological Imaging Année : 2023

Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning

Résumé

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 $ 78.98\pm 5.86\% $ , an F1 score of $ 77.99\pm 5.64\%, $ and an AUC of $ 0.87\pm 0.06 $ . These results set new state-of-the-art benchmarks for this application.

Dates et versions

hal-04342290 , version 1 (13-12-2023)

Identifiants

Citer

Sneha Naik, Roberta Forlano, Pinelopi Manousou, Robert Goldin, Elsa Angelini. Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning. Biological Imaging, 2023, 3, pp.e17. ⟨10.1017/S2633903X23000144⟩. ⟨hal-04342290⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More