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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The ability to explain the prediction of deep learning models to end-users isan important feature to leverage the power of artificial intelligence (AI) forthe medical decision-making process, which is usually considerednon-transparent and challenging to comprehend. In this paper, we applystate-of-the-art eXplainable artificial intelligence (XAI) methods to explainthe prediction of the black-box AI models in the thyroid nodule diagnosisapplication. We propose new statistic-based XAI methods, namely Kernel DensityEstimation and Density map, to explain the case of no nodule detected. XAImethods' performances are considered under a qualitative and quantitativecomparison as feedback to improve the data quality and the model performance.Finally, we survey to assess doctors' and patients' trust in XAI explanationsof the model's decisions on thyroid nodule images.

Towards Trust of Explainable AI in Thyroid Nodule Diagnosis