<ul><li>Given multi-type point maps from different place-types (e.g., tumor regions), researchers aim 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.</li><li>The challenge lies in the spatial variability and inherent heterogeneity observed in spatial properties or arrangements across different place-types.</li><li>The proposed multi-task self-learning framework targets spatial arrangements, utilizing techniques such as spatial mix-up masking and spatial contrastive predictive coding.</li><li>Experimental results on real-world datasets, specifically oncology data, demonstrate that the framework provides higher prediction accuracy compared to baseline methods.</li></ul>

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

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