2020/08/25 by Andrew Wentzel, Wentzel, Andrew, Guadalupe Canahuate +9
Computer Science · #Data Visualization and Analytics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2008.11282
openalex publication_date 2020/08/25 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Advances in data collection in radiation therapy have led to an abundance of\nopportunities for applying data mining and machine learning techniques to\npromote new data-driven insights. In light of these advances, supporting\ncollaboration between machine learning experts and clinicians is important for\nfacilitating better development and adoption of these models. Although many\nmedical use-cases rely on spatial data, where understanding and visualizing the\nunderlying structure of the data is important, little is known about the\ninterpretability of spatial clustering results by clinical audiences. In this\nwork, we reflect on the design of visualizations for explaining novel\napproaches to clustering complex anatomical data from head and neck cancer\npatients. These visualizations were developed, through participatory design,\nfor clinical audiences during a multi-year collaboration with radiation\noncologists and statisticians. We distill this collaboration into a set of\nlessons learned for creating visual and explainable spatial clustering for\nclinical users.\n