CycleGAN for virtual stain transfer: Is seeing really believing?
Fiche publication
Date publication
novembre 2022
Journal
Artificial intelligence in medicine
Auteurs
Membres identifiés du Cancéropôle Est :
Pr WEMMERT Cédric
Tous les auteurs :
Vasiljević J, Nisar Z, Feuerhake F, Wemmert C, Lampert T
Lien Pubmed
Résumé
Digital Pathology is an area prone to high variation due to multiple factors which can strongly affect diagnostic quality and visual appearance of the Whole-Slide-Images (WSIs). The state-of-the art methods to deal with such variation tend to address this through style-transfer inspired approaches. Usually, these solutions directly apply successful approaches from the literature, potentially with some task-related modifications. The majority of the obtained results are visually convincing, however, this paper shows that this is not a guarantee that such images can be directly used for either medical diagnosis or reducing domain shift.This article shows that slight modification in a stain transfer architecture, such as a choice of normalisation layer, while resulting in a variety of visually appealing results, surprisingly greatly effects the ability of a stain transfer model to reduce domain shift. By extensive qualitative and quantitative evaluations, we confirm that translations resulting from different stain transfer architectures are distinct from each other and from the real samples. Therefore conclusions made by visual inspection or pretrained model evaluation might be misleading.
Mots clés
CycleGAN, Digital histopathology, Image-to-image translation, Stain normalisation, Stain transfer
Référence
Artif Intell Med. 2022 11;133:102420