Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology

TL;DR

This paper discusses the development of visual and explainable spatial clustering methods for complex anatomical data in radiation oncology, emphasizing collaboration with clinicians to improve interpretability and adoption.

Contribution

It introduces a participatory design approach for creating visual explanations of spatial clustering tailored for clinical audiences in radiation therapy.

Findings

Developed visualization techniques for spatial clustering in medical data

Gained insights into clinician needs for interpretability

Provided lessons for designing explainable spatial data tools

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.