Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/15775
Title: CycleGAN for virtual stain transfer: Is seeing really believing?
Authors: Vasiljević, Jelica
Nisar Z.
Feuerhake F.
Wemmert, Cedric
Lampert, Thomas
Issue Date: 2022
Abstract: 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.
URI: https://scidar.kg.ac.rs/handle/123456789/15775
Type: article
DOI: 10.1016/j.artmed.2022.102420
ISSN: 0933-3657
SCOPUS: 2-s2.0-85139823627
Appears in Collections:Faculty of Science, Kragujevac

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