AI for Precision Medicine: Histopathology-centered Computational Analysis of Spatial Omics: Integration, Mapping, and Foundation Models

Abstract

Spatial omics (SO) technologies enable spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly place H&E images at the center of SO analysis, bridging morphology with transcriptomic, proteomic, and other spatial molecular modalities. This lecture-style tutorial surveys the algorithmic foundations and recent advances in method development that make them practical and impactful for precision medicine. Following the tutorial flow, we first introduce key SO modalities and data abstractions (tiles/patches, spots, cells, and spatial graphs) and articulate problems to address and motivations, emphasizing multi-scale mismatch, structured spatial dependence, weak supervision, and domain shift across cohorts and sites. We then trace the evolution of modern multimodal representation learning, highlighting graph neural networks, transformer-based architectures, and encoder–decoder designs. The core of the tutorial systematically organizes contemporary methods into three categories: (i) integration methods, which jointly model paired multimodal measurements; (ii) mapping methods, which predict spatial molecular profiles from H&E images; and (iii) foundation models (FMs), which learn transferable representations from large-scale spatial datasets via self-supervised pretraining, contrastive objectives, etc. This tutorial also discusses applications of generative modeling to support imputation and data augmentation. Throughout, we connect methods to real biomedical endpoints (e.g., tumor microenvironment characterization, biomarker discovery, and cohort-level stratification). We further summarize actionable modeling directions enabled by current architectures and delineate persistent gaps driven by data, biology, and technology that are unlikely to be resolved by model design alone. The tutorial concludes with open challenges in interpretability, reliability, privacy, and clinical translation, outlining opportunities for the KDD community to contribute principled data mining and learning approaches to multimodal spatial biology.