English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Histopathological images are essential for medical diagnosis and treatmentplanning, but interpreting them accurately using machine learning can bechallenging due to variations in tissue preparation, staining and imagingprotocols. Domain generalization aims to address such limitations by enablingthe learning models to generalize to new datasets or populations. Styletransfer-based data augmentation is an emerging technique that can be used toimprove the generalizability of machine learning models for histopathologicalimages. However, existing style transfer-based methods can be computationallyexpensive, and they rely on artistic styles, which can negatively impact modelaccuracy. In this study, we propose a feature domain style mixing techniquethat uses adaptive instance normalization to generate style-augmented versionsof images. We compare our proposed method with existing style transfer-baseddata augmentation methods and found that it performs similarly or better,despite requiring less computation and time. Our results demonstrate thepotential of feature domain statistics mixing in the generalization of learningmodels for histopathological image analysis.

Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization