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

Prostate cancer (PCa) is the second deadliest form of cancer in males, and itcan be clinically graded by examining the structural representations of Gleasontissues. This paper proposes \RV{a new method} for segmenting the Gleasontissues \RV{(patch-wise) in order to grade PCa from the whole slide images(WSI).} Also, the proposed approach encompasses two main contributions: 1) Asynergy of hybrid dilation factors and hierarchical decomposition of latentspace representation for effective Gleason tissues extraction, and 2) Athree-tiered loss function which can penalize different semantic segmentationmodels for accurately extracting the highly correlated patterns. In addition tothis, the proposed framework has been extensively evaluated on a large-scalePCa dataset containing 10,516 whole slide scans (with around 71.7M patches),where it outperforms state-of-the-art schemes by 3.22% (in terms of meanintersection-over-union) for extracting the Gleason tissues and 6.91% (in termsof F1 score) for grading the progression of PCa.

A Dilated Residual Hierarchically Fashioned Segmentation Framework for  Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide  Images