ArticleBiomedical optics express2021
Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Cell-MICS: Detecting Immune Cells With Label-Free Two-Photon Autofluorescence and Deep Learning.Journal of biophotonics · 2026Article
- Label-free whole slide virtual multi-staining using dual-excitation photon absorption remote sensing microscopy.Npj imaging · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- FPM2Stain Net: physics-guided super-resolution and multi-modal virtual staining for digital histopathology.Biomedical optics express · 2026Article
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- Non-invasive Imaging Techniques: From Histology to In-vivo Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Deep Learning-Enabled Virtual Multiplexed Immunostaining of Label-Free Tissue for Vascular Invasion Assessment.BME frontiers · 2026Article
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Virtual Histology Staining of Skin Tissue using Ex Vivo Confocal Microscopy and Deep Learning.Journal of biomedical physics & engineering · 2025Article
- Virtual staining of label-free tissue in imaging mass spectrometry.Science advances · 2025Article
- Pixel super-resolved virtual staining of label-free tissue using diffusion models.Nature communications · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Label-Free Evaluation of Lung and Heart Transplant Biopsies Using Tissue Autofluorescence-Based Virtual Staining.BME frontiers · 2025Article
- Low-cost and label-free blue light cystoscopy through digital staining of white light cystoscopy videos.Communications medicine · 2024Article
- Noninvasive Nonlinear Optical Computational Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Virtual histological staining of unlabeled autopsy tissue.Nature communications · 2024Article
- Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images.Scientific reports · 2024Article
- Label-free biomedical optical imaging.Nature photonics · 2023Article
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- Raman spectroscopy and mass spectrometry identifies a unique group of epidermal lipids in active discoid lupus erythematosus.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
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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 OpenQuestion graph.