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Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models


Training machine-learning models with synthetically generated data can alleviate the problem of data scarcity when acquiring diverse and sufficiently large datasets is costly and challenging. Here we show that cascaded diffusion models can be used to synthesize realistic whole-slide image tiles from latent representations of RNA-sequencing data from human tumours. Alterations in gene expression affected the composition of cell types in the generated synthetic image tiles, which accurately preserved the distribution of cell types and maintained the cell fraction observed in bulk RNA-sequencing data, as we show for lung adenocarcinoma, kidney renal papillary cell carcinoma, cervical squamous cell carcinoma, colon adenocarcinoma and glioblastoma. Machine-learning models pretrained with the generated synthetic data performed better than models trained from scratch. Synthetic data may accelerate the development of machine-learning models in scarce-data settings and allow for the imputation of missing data modalities.

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Fig. 1: RNA-CDM model architecture used for generating RNA-seq embeddings and synthetic WSI tiles using diffusion models.
Fig. 2: RNA-to-image multicancer synthetic samples generated by conditioning on the gene-expression latent representation.
Fig. 3: Synthetic samples maintain the cell distributions observed in real-world data.
Fig. 4: Pretraining on synthetic samples improves classification performance in a multicancer classification problem.

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Data availability

TCGA data can be downloaded from the GDC platform ( The two GEO series used in this study can be downloaded from the GEO platform: GSE50760 and GSE226069. The PBTA dataset can be downloaded from the Gabriella Miller Kids First Data Resource Portal (KF-DRC, Microsatellite-instability-status data can be downloaded from the Kaggle platform: Case IDs used for this work as well as the RNA-seq encodings obtained for all experiments are available under an academic-use-only licence at One million synthetic images are available in the Dryad platform at (ref. 77).

Code availability

A demo for generating synthetic images and the code are available under an academic-use-only licence at


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The results published here are in whole or in part based on data generated by the TCGA Research Network ( F.C.-P. was supported by MCIN/AEI/10.13039/501100011033 (grant number PID2021-128317OB-I00), Consejería de Universidad, Investigación e Innovación (grant number P20-00163), which are both funded by ‘ERDF A way of making Europe.’, and a Predoctoral scholarship from the Fulbright Spanish Commission. M.P. was supported by the Belgian American Educational Foundation and FWO (grant number 1161223N). Research reported here was further supported by the National Cancer Institute (NCI) (grant number R01 CA260271). This research used resources of the Argonne Leadership Computing Facility, a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory and is based on research supported by the U.S. DOE Office of Science-Advanced Scientific Computing Research Program, under Contract No. DE-AC02-06CH11357. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations



F.C.-P., M.P. and O.G. conceived and designed the study. F.C.-P., M.P. and Y.Z. performed data preprocessing. F.C.-P. developed the code. T.N.N. and R.M. contributed to code optimization and parallel training. R.M. and T.N.N. provided access to the Argonne National Laboratory platform. J.S. performed the analysis of the clinical impact and analysed the digital pathology quality. Y.Z. obtained the deconvolved RNA-seq data. F.C.-P. and M.P. generated the figures. O.G. supervised the work and obtained the funding. F.C.-P. and O.G. wrote the manuscript with contributions and/or revisions from all authors.

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Correspondence to Olivier Gevaert.

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Stanford has submitted a provisional patent application for this work with patent number 18/538,743, United States, 2023. The authors declare no competing interests.

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Nature Biomedical Engineering thanks Moritz Gerstung, Ke Yuan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Cell-percentage comparison between using bulk RNA-Seq and de-convolved expression.

a. Percentage of lymphocytes cells found by Hovernet in synthetic tiles generated using bulk RNA-Seq and haematopoietic de-convolved RNA-Seq. A significantly higher percentage of lymphocytes was found in all four out of five cancer types with a significantly p-value in four out of five of them (TCGA-CESC p-value = 0.15; TCGA-KIRP p-value = 6.08 × 10−21; TCGA-LUAD p-value = 9.86 × 10−16; TCGA-GBM p-value = 2.02 × 10−7; TCGA-COAD p-value = 1.07 × 10−22). The median difference is annotated in the plot per cancer type. b. UMAP projection of the bulk RNA-Seq expression (circles) and the counterpart deconvolved haematopoietic RNA-Seq (crosses). Clear differences can be observed in the expression, with a mean percentage difference of 7% across the cancer types, which corresponds to a similar increase in lymphocytes in the majority of the cancer types.

Extended Data Fig. 2 Microsatellite-instability-status prediction.

Comparison between a model trained from scratch and a model that have been pretrained using SimCLR on synthetic tiles, on a different number of real tiles sampled from the training set. Metrics are computed on a fivefold CV, and results correspond to those obtained on the different test sets. The model pretrained on the synthetic tiles always outperform the model trained from scratch, no matter the number of training samples that are used.

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Carrillo-Perez, F., Pizurica, M., Zheng, Y. et al. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nat. Biomed. Eng (2024).

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