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

Advances in data collection in radiation therapy have led to an abundance ofopportunities for applying data mining and machine learning techniques topromote new data-driven insights. In light of these advances, supportingcollaboration between machine learning experts and clinicians is important forfacilitating better development and adoption of these models. Although manymedical use-cases rely on spatial data, where understanding and visualizing theunderlying structure of the data is important, little is known about theinterpretability of spatial clustering results by clinical audiences. In thiswork, we reflect on the design of visualizations for explaining novelapproaches to clustering complex anatomical data from head and neck cancerpatients. These visualizations were developed, through participatory design,for clinical audiences during a multi-year collaboration with radiationoncologists and statisticians. We distill this collaboration into a set oflessons learned for creating visual and explainable spatial clustering forclinical users.

Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology