ArticleNature biomedical engineering2025
Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 26 citations in OpenAlex.
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- A generative framework for predicting cellular morphological and transcriptomic perturbation responses.Cell reports methods · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Transcriptomic-guided whole-slide image classification for molecular subtype identification.PLoS computational biology · 2026Article
- Query-driven generative AI synthesizes multi-modal spatial omics from histology.bioRxiv : the preprint server for biology · 2025Article
- Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition.Nature communications · 2025Article
- Computational pathology in precision oncology: Evolution from task-specific models to foundation models.Chinese medical journal · 2025Review
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.Patterns (New York, N.Y.) · 2025Article
- Article
- Pixel super-resolved virtual staining of label-free tissue using diffusion models.Nature communications · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models.Journal of pathology informatics · 2025Article
Corrections and comments
- Update of
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
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.
Indexed as
Identifiers
38514775W4393055385What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.