TY - GEN
T1 - Explainable Spatial Clustering
T2 - 2020 IEEE Visualization Conference, VIS 2020
AU - Wentzel, Andrew
AU - Canahuate, Guadalupe
AU - Van Dijk, Lisanne V.
AU - Mohamed, Abdallah S.R.
AU - Fuller, C. David
AU - Marai, G. Elisabeta
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Collaboration
KW - Data Clustering and Aggregation
KW - Guidelines
KW - Life Sciences
KW - Mixed Initiative Human-Machine Analysis
UR - http://www.scopus.com/inward/record.url?scp=85100750673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100750673&partnerID=8YFLogxK
U2 - 10.1109/VIS47514.2020.00063
DO - 10.1109/VIS47514.2020.00063
M3 - Conference contribution
AN - SCOPUS:85100750673
T3 - Proceedings - 2020 IEEE Visualization Conference, VIS 2020
SP - 281
EP - 285
BT - Proceedings - 2020 IEEE Visualization Conference, VIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 October 2020 through 30 October 2020
ER -