Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning.
Biol Imaging
; 3: e17, 2023.
Article
in En
| MEDLINE
| ID: mdl-38510166
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 , an F1 score of and an AUC of . These results set new state-of-the-art benchmarks for this application.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Biol Imaging
Year:
2023
Document type:
Article
Affiliation country:
United kingdom