HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
Journal of the Royal Statistical Society Series A: Statistics in Society , volume=
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A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.
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HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
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Detecting Breast Carcinoma Metastasis on Whole-Slide Images by Partially Subsampled Multiple Instance Learning
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.