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

Automated analysis of optical colonoscopy (OC) video frames (to assistendoscopists during OC) is challenging due to variations in color, lighting,texture, and specular reflections. Previous methods either remove some of thesevariations via preprocessing (making pipelines cumbersome) or add diversetraining data with annotations (but expensive and time-consuming). We presentCLTS-GAN, a new deep learning model that gives fine control over color,lighting, texture, and specular reflection synthesis for OC video frames. Weshow that adding these colonoscopy-specific augmentations to the training datacan improve state-of-the-art polyp detection/segmentation methods as well asdrive next generation of OC simulators for training medical students. The codeand pre-trained models for CLTS-GAN are available on Computational EndoscopyPlatform GitHub (https://github.com/nadeemlab/CEP).

CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy