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/

Zendy Plus

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

Zendy Tools

Zendy Open

Accurately identifying patient's status through medical images plays animportant role in diagnosis and treatment. Artificial intelligence (AI),especially the deep learning, has achieved great success in many fields.However, more reliable AI model is needed in image guided diagnosis andtherapy. To achieve this goal, developing a balanced, safe and robust modelwith a unified framework is desirable. In this study, a new unified modeltermed as automated multi-objective Mixer (AutoMO-Mixer) model was developed,which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer)as base. To build a balanced model, sensitivity and specificity were consideredas the objective functions simultaneously in training stage. Meanwhile, a newevidential reasoning based on entropy was developed to achieve a safe androbust model in testing stage. The experiment on an optical coherencetomography dataset demonstrated that AutoMO-Mixer can obtain safer, morebalanced, and robust results compared with MLP-Mixer and other availablemodels.

AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine