GESTALT: Fast, Generalizable Histopathology Image Synthesis from Gene Expression with Pretrained Pathology Foundation Models
Felix Ho, Alexandros Graikos, Dimitris Samaras
Abstract
Spatial transcriptomics measures gene expression while preserving where each cell sits in the tissue. The inverse, what a given transcriptomic state looks like, is less explored but equally useful: it lets researchers visualize the morphological consequence of a molecular state and interpret a spatial assay's molecular calls against real tissue structure. GeneFlow, the current state of the art on the Xenium gene expression-to-image benchmark, trains a model from scratch, incurring a high cost on every new target dataset it is applied to. We instead propose GESTALT, which reformulates gene expression-to-image synthesis as predicting a pathology foundation model's image embedding. A lightweight gene-token transformer predicts this embedding from multi-cell gene expression, and a frozen or LoRA-adapted diffusion decoder (PixCell) renders it as an image. Because this visual knowledge is reused rather than relearned, GESTALT trains in a fraction of the time. In both multi-cell and single-cell settings, our method outperforms the SoTA on perceptual metrics across all three Xenium datasets. Beyond improved image quality, GESTALT natively synthesizes at 1024 x 1024, and is evaluated in three generalization settings: zero-shot transfer across gene panels and datasets, held-out patients and disease states, and transfer across studies and tissue sources. Coupled with a statistical model of spatial expression, GESTALT also provides, to our knowledge, the first paired synthesis of cell-resolved transcriptomic fields and tissue images with explicit control over cell type, density, and composition. Code is available at https://github.com/trongnhandev1011/Gestalt.