ArticleDiagnostic pathology2021
Normalization of HE-stained histological images using cycle consistent generative adversarial networks.
Article in Diagnostic pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
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- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- A toolkit for generating virtual brightfield images of histological and immunohistochemical stains from multiplexed data with AI-based channel selection and image enhancement.Frontiers in bioinformatics · 2026Article
- Domain-adaptive semi-supervised learning for efficient rare pathological lesion detection with minimal annotation.NPJ digital medicine · 2025Article
- Virtual Histology Staining of Skin Tissue using Ex Vivo Confocal Microscopy and Deep Learning.Journal of biomedical physics & engineering · 2025Article
- Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.Sensors (Basel, Switzerland) · 2025Article
- The intelligent development and preservation of folk sports culture under artificial intelligence.Scientific reports · 2025Article
- Astragaloside IV Alleviates Osteoarthritis by Inhibiting Chondrocyte Ferroptosis via the p53/SLC7A11/GPX4 Axis.Journal of inflammation research · 2025Article
- Illustration image style transfer method design based on improved cyclic consistent adversarial network.PloS one · 2025Article
- Astragaloside IV Alleviates Systemic Lupus Erythematosus by Modulating ITGB1/PTK2/p38 Pathway: Integrated Network Pharmacology and Experimental Validation.Drug design, development and therapy · 2025Article
- Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.European heart journal. Imaging methods and practice · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump.Journal of pathology informatics · 2024Article
- Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.Science advances · 2024Article
- Predicting Mismatch Repair Deficiency Status in Endometrial Cancer through Multi-Resolution Ensemble Learning in Digital Pathology.Journal of imaging informatics in medicine · 2024Article
- Learning generalizable AI models for multi-center histopathology image classification.NPJ precision oncology · 2024Article
- Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images.Scientific reports · 2024Article
- Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.Scientific reports · 2023Article
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6 authors.
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Abstract
backgroundHistological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques.
methodsIn this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network G
resultsQualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%.
conclusionsCycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch . The data set supporting the solutions is available at https://doi.org/10.11588/data/8LKEZF .
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