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 image is important for breast cancer screening, and typicallyobtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique(MLO), to provide complementary information. However, previous methods mostlylearn features from the two views independently, which violates the clinicalknowledge and ignores the importance of dual-view correlation. In this paper,we propose a dual-view correlation hybrid attention network (DCHA-Net) forrobust holistic mammogram classification. Specifically, DCHA-Net is carefullydesigned to extract and reinvent deep features for the two views, and meanwhileto maximize the underlying correlations between them. A hybrid attentionmodule, consisting of local relation and non-local attention blocks, isproposed to alleviate the spatial misalignment of the paired views in thecorrelation maximization. A dual-view correlation loss is introduced tomaximize the feature similarity between corresponding strip-like regions withequal distance to the chest wall, motivated by the fact that their featuresrepresent the same breast tissues, and thus should be highly-correlated.Experimental results on two public datasets, i.e., INbreast and CBIS-DDSM,demonstrate that DCHA-Net can well preserve and maximize feature correlationsacross views, and thus outperforms the state-of-the-arts for classifying awhole mammogram as malignant or not.

Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification