论文标题
可解释的空间聚类：利用辐射肿瘤的空间数据
Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology
论文作者
论文摘要
放射疗法的数据收集进步导致了应用数据挖掘和机器学习技术的大量机会，以促进新的数据驱动的见解。鉴于这些进步，支持机器学习专家和临床医生之间的合作对于促进这些模型的更好开发和采用至关重要。尽管许多医疗用例都依赖于空间数据，但是了解和可视化数据的潜在结构很重要，但对临床受众的空间聚类结果的可解释性知之甚少。在这项工作中，我们反思了可视化设计的设计，以解释从头颈癌患者中聚集复杂的解剖学数据的新方法。这些可视化是通过参与性设计在与放射线肿瘤学家和统计学家的多年合作期间为临床观众开发的。我们将这种合作提炼成一组学习的经验教训，用于为临床用户创建视觉和可解释的空间聚类。
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.
