Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable Spatial Clustering: Leveraging Spatial Data in Radiation
  Oncology   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.

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The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
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The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess