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

Mammogram mass detection is crucial for diagnosing and preventing the breastcancers in clinical practice. The complementary effect of multi-view mammogramimages provides valuable information about the breast anatomical priorstructure and is of great significance in digital mammography interpretation.However, unlike radiologists who can utilize the natural reasoning ability toidentify masses based on multiple mammographic views, how to endow the existingobject detection models with the capability of multi-view reasoning is vitalfor decision-making in clinical diagnosis but remains the boundary to explore.In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN),which is tailored for mammogram mass detection and endows existing detectionmethods with multi-view reasoning ability. The proposed AGN consists of threesteps. Firstly, we introduce a Bipartite Graph convolutional Network (BGN) tomodel the intrinsic geometric and semantic relations of ipsilateral views.Secondly, considering that the visual asymmetry of bilateral views is widelyadopted in clinical practice to assist the diagnosis of breast lesions, wepropose an Inception Graph convolutional Network (IGN) to model the structuralsimilarities of bilateral views. Finally, based on the constructed graphs, themulti-view information is propagated through nodes methodically, which equipsthe features learned from the examined view with multi-view reasoning ability.Experiments on two standard benchmarks reveal that AGN significantly exceedsthe state-of-the-art performance. Visualization results show that AGN providesinterpretable visual cues for clinical diagnosis.

Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection