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

Exploiting learning algorithms under scarce data regimes is a limitation anda reality of the medical imaging field. In an attempt to mitigate the problem,we propose a data augmentation protocol based on generative adversarialnetworks. We condition the networks at a pixel-level (segmentation mask) and ata global-level information (acquisition environment or lesion type). Suchconditioning provides immediate access to the image-label pairs whilecontrolling global class specific appearance of the synthesized images. Tostimulate synthesis of the features relevant for the segmentation task, anadditional passive player in a form of segmentor is introduced into theadversarial game. We validate the approach on two medical datasets: BraTS,ISIC. By controlling the class distribution through injection of syntheticimages into the training set we achieve control over the accuracy levels of thedatasets' classes.

Red-GAN: Attacking class imbalance via conditioned generation. Yet  another perspective on medical image synthesis for skin lesion dermoscopy and  brain tumor MRI