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

Diagnosis of breast carcinomas has so far been limited to the morphologicalinterpretation of epithelial cells and the assessment of epithelial tissuearchitecture. Consequently, most of the automated systems have focused oncharacterizing the epithelial regions of the breast to detect cancer. In thispaper, we propose a system for classification of hematoxylin and eosin (H&E)stained breast specimens based on convolutional neural networks that primarilytargets the assessment of tumor-associated stroma to diagnose breast cancerpatients. We evaluate the performance of our proposed system using a largecohort containing 646 breast tissue biopsies. Our evaluations show that theproposed system achieves an area under ROC of 0.92, demonstrating thediscriminative power of previously neglected tumor-associated stroma as adiagnostic biomarker.

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images