Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
This work addresses the need for explainable AI in medical applications, specifically for clinicians using spatial data, but it is incremental as it focuses on design lessons rather than new algorithmic breakthroughs.
The paper tackled the problem of making spatial clustering results interpretable for clinical audiences in radiation oncology, resulting in a set of design lessons for visualizations developed through participatory collaboration with clinicians.
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.