Title: GeneAR: Autoregressive Gene-to-WSI Tile Synthesis via Causal MeanFlow
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Gene-to-WSI Tile Synthesis, Causal Inference, Visual Autoregressive Modeling

Abstract: Understanding how transcriptomic programs shape tissue morphology remains a central challenge in computational pathology. Gene-to-WSI tile synthesis offers a principled generative framework to translate molecular profiles into histological images. However, most existing methods compress RNA-Seq into a single global embedding injected once at initialization, an oversimplified design that weakens transcriptomic signals and induces non-causal associations between gene expression and tissue morphology. We present GeneAR, an Autoregressive Gene-to-WSI model that reformulates synthesis as an iterative, coarse-to-fine generative process. At its core is a novel Causal MeanFlow module that reinforces transcriptome-informed guidance at multiple stages and mitigates non-causal factors through counterfactual-style interventions, thereby ensuring biological fidelity throughout the generative trajectory. Combined with a β-VAE for compact gene embeddings and a multi-scale vector quantizer for discrete morphology representation, GeneAR generates H&E-stained WSI tiles that are both visually realistic and transcriptomically faithful. Extensive experiments across five TCGA cancer benchmarks demonstrate consistent state-of-the-art performance, surpassing prior methods in both generative fidelity and downstream classification accuracy. All models and code will be released to facilitate reproducibility.

TL;DR: A Novel Visual Autoregressive Framework with Causal MeanFlow for WSI Tile Generation