  The goal of spatial-configuration-based classification is to build a
classifier to distinguish two classes (e.g., responder, non-responder) based on
the spatial arrangements (e.g., spatial interactions between different point
categories) given multi-category point data from two classes. This problem is
important for generating hypotheses in medical pathology towards discovering
new immunotherapies for cancer treatment as well as for other applications in
biomedical research and microbial ecology. This problem is challenging due to
an exponential number of category subsets which may vary in the strength of
spatial interactions. Most prior efforts on using human selected spatial
association measures may not be sufficient for capturing the relevant (e.g.,
surrounded by) spatial interactions which may be of biological significance. In
addition, the related deep neural networks are limited to category pairs and do
not explore larger subsets of point categories. To overcome these limitations,
we propose a Spatial-interaction Aware Multi-Category deep neural Network
(SAMCNet) architecture and contribute novel local reference frame
characterization and point pair prioritization layers for
spatial-configuration-based classification. Extensive experimental results on
multiple cancer datasets show that the proposed architecture provides higher
prediction accuracy over baseline methods.
