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

2025-01-20

AI Summary

Domain shift arising from spatial heterogeneity in cross-regional tumor spatial omics data hinders generalization of machine learning models. Method: We propose a domain-adaptive binary spatial point pattern classification framework, featuring a novel multi-task self-supervised learning architecture that jointly integrates spatial shuffling masking and spatial contrastive predictive coding to explicitly model inter-point spatial structural relationships and mitigate geometric distribution discrepancies between source and target domains. Contribution/Results: Evaluated on real-world tumor spatial transcriptomics datasets, our method achieves significant improvements over state-of-the-art baselines in cross-subregion classification tasks—yielding substantial accuracy gains. The approach provides interpretable, robust computational support for generating novel, spatial-heterogeneity-driven hypotheses in immuno-oncology, particularly for spatially informed therapeutic design.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Multi-modal Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Artificial Intelligence in Medicine
• Cancer Data Classification
• Transfer Learning in AI

Innovation

• Spatial Aliasing
• Spatial Contrast Encoding
• Cancer Data Classification