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2019 | OriginalPaper | Buchkapitel

Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study

verfasst von : Michael Gadermayr, Barbara M. Klinkhammer, Peter Boor

Erschienen in: Digital Pathology

Verlag: Springer International Publishing

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Abstract

Approaches relying on adversarial networks facilitate image-to-image-translation based on unpaired training and thereby open new possibilities for special tasks in image analysis. We propose a methodology to improve segmentability of histological images by making use of image-to-image translation. We generate virtual stains and exploit the additional information during segmentation. Specifically a very basic pixel-based segmentation approach is applied in order to focus on the information content available on pixel-level and to avoid any bias which might be introduced by more elaborated techniques. The results of this proof-of-concept trial indicate a performance gain compared to segmentation with the source stain only. Further experiments including more powerful supervised state-of-the-art machine learning approaches and larger evaluation data sets need to follow.

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Fußnoten
1
We use the provided PyTorch reference implementation [12].
 
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Metadaten
Titel
Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study
verfasst von
Michael Gadermayr
Barbara M. Klinkhammer
Peter Boor
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-23937-4_5