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

Deep learning for medical imaging is limited by data scarcity and domainshift, which lead to biased training sets that do not accurately representdeployment conditions. A related practical problem is cross-modal segmentationwhere the objective is to segment unlabelled domains using previously labelledimages from other modalites, which is the context of the MICCAI CrossMoDA 2022challenge on vestibular schwannoma (VS) segmentation. In this context, wepropose a VS segmentation method that leverages conventional image-to-imagetranslation and segmentation using iterative self training combined to adedicated data augmentation technique called Generative Blending Augmentation(GBA). GBA is based on a one-shot 2D SinGAN generative model that allows torealistically diversify target tumor appearances in a downstream segmentationmodel, improving its generalization power at test time. Our solution rankedfirst on the VS segmentation task during the validation and test phase of theCrossModa 2022 challenge.

Cross-modal tumor segmentation using generative blending augmentation and self training