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Jonathan Xu's SpatialFinder framework combines a biomedical vision-language model with human-in-the-loop optimization to predict gene expression heterogeneity and rank high-value regions of interest (ROIs) across H&E tissue slides. The dataset, last updated in April 2026, contains results from evaluating the framework across four Visium HD tissue types, where it outperformed baseline models for ROI ranking. The framework aims to make spatial transcriptomics more cost-effective and clinically actionable.
File format is ZIP; contents require inspection. The dataset is small (3.8 MB), indicating it is not raw spatial transcriptomics data.