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 classification remains a challenging task due to inter-classambiguity and intra-class variability. Existing deep learning-based methods tryto confront this challenge by utilizing complex nonlinear projections. However,these methods typically extract global features from entire images, neglectingthe fact that the subtle detail information can be crucial in extractingdiscriminative features. In this study, we propose a novel method namedAttention Model Enhanced Network (AMEN), which is formulated in a multi-branchfashion with pixel-wised attention model and classification submodular.Specifically, the feature learning part in AMEN can generate pixel-wisedattention map, while the classification submodular are utilized to classify thesamples. To focus more on subtle detail information, the sample image isenhanced by the pixel-wised attention map generated from former branch.Furthermore, boosting strategy are adopted to fuse classification results fromdifferent branches for better performance. Experiments conducted on threebenchmark datasets demonstrate the superiority of the proposed method undervarious scenarios.

Attention Model Enhanced Network for Classification of Breast Cancer Image