ArticleNature biomedical engineering2022
A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
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- A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging.NPJ artificial intelligence · 2026Article
- Contrastive learning enhances fairness in pathology artificial intelligence systems.Cell reports. Medicine · 2025Article
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- AI-augmented intraoperative decision-making workflows in diffuse midline glioma biopsy using cryosection pathology.Nature communications · 2025Article
- Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention.Scientific reports · 2025Article
- An end-to-end multifunctional AI platform for intraoperative diagnosis.NPJ digital medicine · 2025Article
- H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking.NPJ digital medicine · 2025Review
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.Breast cancer research : BCR · 2025Article
- H&E Style Translation Using CycleGAN for Deep Ultraviolet-Excitation Fluorescence Images of Pancreatic Endoscopic Ultrasound-Fine Needle Aspiration/Biopsy Toward Slide-Free Rapid Pathology.Acta histochemica et cytochemica · 2025Article
- A data-efficient strategy for building high-performing medical foundation models.Nature biomedical engineering · 2025Article
- GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessment.International journal of oral science · 2025Article
- Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography.Light, science & applications · 2025Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12-48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.
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