Conference proceeding
Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
2020 IEEE Visualization Conference (VIS), pp.281-285
10/2020
DOI: 10.1109/VIS47514.2020.00063
Abstract
Advances in data collection in radiation therapy have led to an abundance of opportunities for applying data mining and machine learning techniques to promote new data-driven insights. In light of these advances, supporting collaboration between machine learning experts and clinicians is important for facilitating better development and adoption of these models. Although many medical use-cases rely on spatial data, where understanding and visualizing the underlying structure of the data is important, little is known about the interpretability of spatial clustering results by clinical audiences. In this work, we reflect on the design of visualizations for explaining novel approaches to clustering complex anatomical data from head and neck cancer patients. These visualizations were developed, through participatory design, for clinical audiences during a multi-year collaboration with radiation oncologists and statisticians. We distill this collaboration into a set of lessons learned for creating visual and explainable spatial clustering for clinical users.
Details
- Title: Subtitle
- Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
- Creators
- Andrew Wentzel - University of Illinois ChicagoGuadalupe Canahuate - University of IowaLisanne V van Dijk - University of Texas at AustinAbdallah S.R Mohamed - University of Texas at AustinC. David Fuller - University of Texas at AustinG. Elisabeta Marai - University of Illinois Chicago
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE Visualization Conference (VIS), pp.281-285
- DOI
- 10.1109/VIS47514.2020.00063
- Publisher
- IEEE
- Language
- English
- Date published
- 10/2020
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197113402771
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