Data augmentation based on spatial deformations for histopathology: An evaluation in the context of glomeruli segmentation.
Fiche publication
Date publication
juin 2022
Journal
Computer methods and programs in biomedicine
Auteurs
Membres identifiés du Cancéropôle Est :
Pr WEMMERT Cédric
Tous les auteurs :
Allender F, Allègre R, Wemmert C, Dischler JM
Lien Pubmed
Résumé
The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations.
Mots clés
Data augmentation, Glomeruli segmentation, Histopathological images, Random spatial deformations
Référence
Comput Methods Programs Biomed. 2022 06;221:106919