ArticleFrontiers in bioinformatics2026
SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sequencing an entire spatial transcriptomics slide can cost thousands of dollars per assay, making routine use impractical. Focusing on smaller regions of interest (ROIs) based on adjacent H&E slides offers a practical alternative, but there is (i) no reliable way to identify the most informative areas from standard H&E images alone; and (ii) limited solutions for clinicians to prioritize the microenvironment of their own interests. Here we introduce SpatialFinder, a framework that combines a biomedical vision-language model (VLM) with a human-in-the-loop optimization pipeline to predict gene expression heterogeneity and rank high-value ROIs across routine H&E tissue slides. Evaluated across four Visium HD tissue types, SpatialFinder consistently outperforms VLM-only baselines for both diversity- and tumor-targeted ROI ranking, achieving Spearman's
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