Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data

TL;DR

This paper introduces a novel multi-task self-learning framework that leverages spatial arrangements in point map data to improve domain-adapted classification accuracy, particularly applied to oncology datasets for cancer research.

Contribution

It proposes a new framework focusing on spatial arrangements, addressing limitations of previous domain-invariant feature learning methods in spatially heterogeneous data.

Findings

Higher prediction accuracy on oncology datasets

Effective handling of spatial heterogeneity

Outperforms baseline methods in experiments

Abstract

Given multi-type point maps from different place-types (e.g., tumor regions), our objective is to develop a classifier trained on the source place-type to accurately distinguish between two classes of the target place-type based on their point arrangements. This problem is societally important for many applications, such as generating clinical hypotheses for designing new immunotherapies for cancer treatment. The challenge lies in the spatial variability, the inherent heterogeneity and variation observed in spatial properties or arrangements across different locations (i.e., place-types). Previous techniques focus on self-supervised tasks to learn domain-invariant features and mitigate domain differences; however, they often neglect the underlying spatial arrangements among data points, leading to significant discrepancies across different place-types. We explore a novel multi-task self-learning framework that targets spatial arrangements, such as spatial mix-up masking and spatial contrastive predictive coding, for spatially-delineated domain-adapted AI classification. Experimental results on real-world datasets (e.g., oncology data) show that the proposed framework provides higher prediction accuracy than baseline methods.