Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data
This work addresses the challenge of analyzing immune-tumor relationships in oncology for designing immunotherapies, but it is incremental as it builds on existing non-Euclidean DNN methods with specific enhancements.
The paper tackled the problem of classifying point sets in non-Euclidean space, such as oncology data, by developing a spatially-lucid classifier that handles spatial variability and interpretability, achieving higher prediction accuracy than baseline methods on real-world datasets.
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.