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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Colorectal cancer is a leading cause of cancer death for both men and women.For this reason, histopathological characterization of colorectal polyps is themajor instrument for the pathologist in order to infer the actual risk forcancer and to guide further follow-up. Colorectal polyps diagnosis includes theevaluation of the polyp type, and more importantly, the grade of dysplasia.This latter evaluation represents a critical step for the clinical follow-up.The proposed deep learning-based classification pipeline is based onstate-of-the-art convolutional neural network, trained using propercountermeasures to tackle WSI high resolution and very imbalanced dataset. Theexperimental results show that one can successfully classify adenomas dysplasiagrade with 70% accuracy, which is in line with the pathologists' concordance.

Dysplasia grading of colorectal polyps through CNN analysis of WSI