  Given multi-category point sets from different place-types, our goal is to
develop a spatially-lucid classifier that can distinguish between two classes
based on the arrangements of their points. This problem is important for many
applications, such as oncology, for analyzing immune-tumor relationships and
designing new immunotherapies. It is challenging due to spatial variability and
interpretability needs. Previously proposed techniques require dense training
data or have limited ability to handle significant spatial variability within a
single place-type. Most importantly, these deep neural network (DNN) approaches
are not designed to work in non-Euclidean space, particularly point sets.
Existing non-Euclidean DNN methods are limited to one-size-fits-all approaches.
We explore a spatial ensemble framework that explicitly uses different training
strategies, including weighted-distance learning rate and spatial domain
adaptation, on various place-types for spatially-lucid classification.
Experimental results on real-world datasets (e.g., MxIF oncology data) show
that the proposed framework provides higher prediction accuracy than baseline
methods.
