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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Breast cancer is a major concern for women's health globally, with axillarylymph node (ALN) metastasis identification being critical for prognosisevaluation and treatment guidance. This paper presents a deep learning (DL)classification pipeline for quantifying clinical information from digitalcore-needle biopsy (CNB) images, with one step less than existing methods. Apublicly available dataset of 1058 patients was used to evaluate theperformance of different baseline state-of-the-art (SOTA) DL models inclassifying ALN metastatic status based on CNB images. An extensive ablationstudy of various data augmentation techniques was also conducted. Finally, themanual tumor segmentation and annotation step performed by the pathologists wasassessed.

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis