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

Breast cancer is the most common cancer in women, and hundreds of thousandsof unnecessary biopsies are done around the world at a tremendous cost. It iscrucial to reduce the rate of biopsies that turn out to be benign tissue. Inthis study, we build deep neural networks (DNNs) to classify biopsied lesionsas being either malignant or benign, with the goal of using these networks assecond readers serving radiologists to further reduce the number of falsepositive findings. We enhance the performance of DNNs that are trained to learnfrom small image patches by integrating global context provided in the form ofsaliency maps learned from the entire image into their reasoning, similar tohow radiologists consider global context when evaluating areas of interest. Ourexperiments are conducted on a dataset of 229,426 screening mammography examsfrom 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464benign and 136 malignant lesions.

Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms