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

In clinical practice, human radiologists actually review medical images withhigh resolution monitors and zoom into region of interests (ROIs) for aclose-up examination. Inspired by this observation, we propose a hierarchicalgraph neural network to detect abnormal lesions from medical images byautomatically zooming into ROIs. We focus on mammogram analysis for breastcancer diagnosis for this study. Our proposed network consist of two graphattention networks performing two tasks: (1) node classification to predictwhether to zoom into next level; (2) graph classification to classify whether amammogram is normal/benign or malignant. The model is trained and evaluated onINbreast dataset and we obtain comparable AUC with state-of-the-art methods.

Zoom in to where it matters: a hierarchical graph based model for mammogram analysis