Aljehani Mohammad R, Alamri Fouad H, Elyas MEK, Almohammadi AS, Alanazi ASA, Alharbi MA. The importance of histopathological evaluation in cancer diagnosis and treatment. Int J Health Sci (Qassim) Universidad Tecnica de Manabi. 2023;7:3614–23. https://doi.org/10.53730/ijhs.v7ns1.15270.
Yan F, Wu J, Li J, Wang W, Chen Y, Wei L, et al. PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks. NPJ Digit Med Nat Res. 2025;8. https://doi.org/10.1038/s41746-025-02027-w.
Mebratie DY, Dagnaw GG. Review of immunohistochemistry techniques: Applications, current status, and future perspectives. Semin Diagn Pathol [Internet]. Volume 41. W.B. Saunders; 2024. pp. 154–60. [cited 2026 Feb 5];. https://doi.org/10.1053/j.semdp.2024.05.001.
Alturkistani HA, Tashkandi FM, Mohammedsaleh ZM. Histological Stains: A Literature Review and Case Study. Glob J Health Sci. 2015;72–9. https://doi.org/10.5539/gjhs.v8n3p72.
Hanna MG, Ardon O, Reuter VE, Sirintrapun SJ, England C, Klimstra DS, et al. Integrating digital pathology into clinical practice. Modern Pathology. Springer Nature; 2022. pp. 152–64. https://doi.org/10.1038/s41379-021-00929-0.
Hoque MZ, Keskinarkaus A, Nyberg P, Seppänen T. Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison. Inform Fusion Elsevier B V. 2024;102. https://doi.org/10.1016/j.inffus.2023.101997.
Rivenson Y, Liu T, Wei Z, Zhang Y, de Haan K, Ozcan A. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci Appl Nat Publishing Group. 2019;8. https://doi.org/10.1038/s41377-019-0129-y.
Wang R, Song P, Jiang S, Yan C, Zhu J, Guo C, et al. Virtual brightfield and fluorescence staining for Fourier ptychography via unsupervised deep learning. Opt Lett Optica Publishing Group. 2020;45:5405. https://doi.org/10.1364/ol.400244.
Rivenson Y, Wang H, Wei Z, de Haan K, Zhang Y, Wu Y, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng Nat Publishing Group. 2019;3:466–77. https://doi.org/10.1038/s41551-019-0362-y.
Li J, Garfinkel J, Zhang X, Wu D, Zhang Y, de Haan K, et al. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl Springer Nat. 2021;10. https://doi.org/10.1038/s41377-021-00674-8.
Bai B, Yang X, Li Y, Zhang Y, Pillar N, Ozcan A. Deep learning-enabled virtual histological staining of biological samples. Light Sci. Appl. Springer Nature; 2023. https://doi.org/10.1038/s41377-023-01104-7
Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol. Artif. Intell. Radiological Society of North America Inc.; 2020. https://doi.org/10.1148/ryai.2020200029.
Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD + AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. BMJ Publishing Group; 2024. https://doi.org/10.1136/bmj-2023-078378.
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, Chan AW, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med Nat Res. 2020;26:1364–74. https://doi.org/10.1038/s41591-020-1034-x.
Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ, Darzi A, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med Nat Res. 2020;26:1351–63. https://doi.org/10.1038/s41591-020-1037-7.
Hong Z, Yue Y, Chen Y, Cong L, Lin H, Luo Y et al. Out-of-distribution Detection in Medical Image Analysis: A survey. 2024; http://arxiv.org/abs/2404.18279
Fraggetta F, L’imperio V, Ameisen D, Carvalho R, Leh S, Kiehl TR, et al. Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory by the european society of digital and integrative pathology (ESDIP). Diagnostics. Multidisciplinary Digital Publishing Institute (MDPI); 2021. p. 11. https://doi.org/10.3390/diagnostics11112167.
Lafriniere M, Bui MM, Evans AJ, Parwani AV, Nasim M, Lott R, et al. Practical Tips to Assist Implementation of Whole Slide Imaging. Contributors/Acknowledgements Declaration of Conflicting Interests; 2025.
Assi H, Cao R, Castelino M, Cox B, Gilbert FJ, Gröhl J, et al. A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption. Photoacoustics. Elsevier GmbH; 2023. https://doi.org/10.1016/j.pacs.2023.100539.
John S, Hester S, Basij M, Paul A, Xavierselvan M, Mehrmohammadi M, et al. Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast. Photoacoustics. Elsevier GmbH; 2023. https://doi.org/10.1016/j.pacs.2023.100533.
Shaban MT, Baur C, Navab N, Albarqouni S, StainGAN. Stain Style Transfer for Digital Histological Images. 2018; http://arxiv.org/abs/1804.01601
Latonen L, Koivukoski S, Khan U, Ruusuvuori P. Virtual staining for histology by deep learning. Trends Biotechnol. Elsevier Ltd; 2024. pp. 1177–91. https://doi.org/10.1016/j.tibtech.2024.02.009.
Hussain MA, Waris MA, Akram MU, Khan MJ, Asaf MZ, Javaid A, et al. VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology. Sci Rep Nat Res. 2025;15. https://doi.org/10.1038/s41598-025-26493-0.
Zhang G, Ning B, Hui H, Yu T, Yang X, Zhang H, et al. Image-to-Images Translation for Multiple Virtual Histological Staining of Unlabeled Human Carotid Atherosclerotic Tissue. Mol Imaging Biol Springer Sci Bus Media Deutschland GmbH. 2022;24:31–41. https://doi.org/10.1007/s11307-021-01641-w.
Misra S, Na S, Park K, Yoon C, Misra S, Kim C, et al. Deep learning based label-free virtual staining and classification of human tissues using digital slide scanner. Med Image Anal Elsevier B V. 2026;108. https://doi.org/10.1016/j.media.2025.103865.
Li D, Hui H, Zhang Y, Tong W, Tian F, Yang X, et al. Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue. Mol Imaging Biol Springer Sci Bus Media Deutschland GmbH. 2020;22:1301–9. https://doi.org/10.1007/s11307-020-01508-6.
Kreiss L, Jiang S, Li X, Xu S, Zhou KC, Lee KC et al. Digital staining in optical microscopy using deep learning - a review. PhotoniX. Springer; 2023. https://doi.org/10.1186/s43074-023-00113-4
Bai B, Wang H, Li Y, de Haan K, Colonnese F, Wan Y et al. Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning. BME Front. American Association for the Advancement of Science; 2022;2022. https://doi.org/10.34133/2022/9786242
Zhang Y, Huang L, Liu T, Cheng K, de Haan K, Li Y et al. Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks. Intelligent Computing. American Association for the Advancement of Science; 2022;2022. https://doi.org/10.34133/2022/9818965
Shi L, Hou X, Lai JKW, Wong IHM, Huang B, Hui ALY, et al. UniStain: A unified and organ-aware virtual H&E staining framework for label-free autofluorescence images. Artif Intell Med Elsevier B V. 2026;173. https://doi.org/10.1016/j.artmed.2025.103335.
Zhang Y, Kang L, Wong IHM, Dai W, Li X, Chan RCK et al. High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning. Advanced Science. John Wiley and Sons Inc; 2022;9. https://doi.org/10.1002/advs.202102358
Yang X, Bai B, Zhang Y, Aydin M, Li Y, Selcuk SY, et al. Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning. Nat Commun Nat Res. 2024;15. https://doi.org/10.1038/s41467-024-52263-z.
Zhang Y, Huang L, Pillar N, Li Y, Chen H, Ozcan A. Pixel super-resolved virtual staining of label-free tissue using diffusion models. Nat Commun Nat Res. 2025;16. https://doi.org/10.1038/s41467-025-60387-z.
Li Y, Pillar N, Li J, Liu T, Wu D, Sun S, et al. Virtual histological staining of unlabeled autopsy tissue. Nat Commun Nat Res. 2024;15. https://doi.org/10.1038/s41467-024-46077-2.
Wang Q, Akram AR, Dorward DA, Talas S, Monks B, Thum C, et al. Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images. NPJ Imaging Springer Nat. 2024;2. https://doi.org/10.1038/s44303-024-00021-7.
Park J, Shin SJ, Kim G, Cho H, Ryu D, Ahn D, et al. Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining. Nat Commun Nat Res. 2025;16. https://doi.org/10.1038/s41467-025-59820-0.
Fang T, Wu Z, Chen X, Tan L, Li Z, Ji J, Sons Inc. Advanced Intelligent Systems. John Wiley and ; 2024. p. 6. Label-Free Virtual Peritoneal Lavage Cytology via Deep-Learning-Assisted Single-Color Stimulated Raman Scattering Microscopy. https://doi.org/10.1002/aisy.202300689
Zhu R, He H, Chen Y, Yi M, Ran S, Wang C et al. Deep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images. Comput Biol Med. Elsevier Ltd; 2024;180. https://doi.org/10.1016/j.compbiomed.2024.108958
Zhang H, Pan M, Zhang C, Xu C, Qi H, Lei D, et al. ULST: U-shaped LeWin Spectral Transformer for virtual staining of pathological sections. Computerized Medical Imaging and Graphics. Elsevier Ltd; 2025. p. 123. https://doi.org/10.1016/j.compmedimag.2025.102534.
McNeil C, Wong PF, Sridhar N, Wang Y, Santori C, Wu CH, et al. An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology. Mod Pathol Elsevier B V. 2024;37. https://doi.org/10.1016/j.modpat.2023.100377.
Wong PF, McNeil C, Wang Y, Paparian J, Santori C, Gutierrez M, et al. Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer. Mod Pathol Elsevier B V. 2024;37. https://doi.org/10.1016/j.modpat.2024.100573.
Zhang Y, Huang L, Pillar N, Li Y, Li Y, Migas LG, A P, P L I E D S C I E N C E, S A N D E N G I N E E R. I N G Virtual staining of label-free tissue in imaging mass spectrometry [Internet]. Sci. Adv. 2025. https://www.science.org
Wang Y, Guan N, Li J, Wang X. A Virtual Staining Method Based on Self-Supervised GAN for Fourier Ptychographic Microscopy Colorful Imaging. Applied Sciences (Switzerland). Multidisciplinary Digital Publishing Institute (MDPI); 2024. p. 14. https://doi.org/10.3390/app14041662.
Chen M, Liu YT, Khan FS, Fox MC, Reichenberg JS, Lopes FCPS, et al. Single color digital H&E staining with In-and-Out Net. Computerized Medical Imaging and Graphics. Elsevier Ltd; 2024. p. 118. https://doi.org/10.1016/j.compmedimag.2024.102468.
Li X, Liu H, Song X, Marboe CC, Brott BC, Litovsky SH, et al. Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images. J Biomed Opt SPIE-Intl Soc Opt Eng. 2024;29. https://doi.org/10.1117/1.jbo.29.3.036004.
Yin Z, He B, Ying Y, Zhang S, Yang P, Chen Z, et al. Fast and label-free 3D virtual H&E histology via active phase modulation-assisted dynamic full-field OCT. NPJ Imaging Springer Nat. 2025;3. https://doi.org/10.1038/s44303-025-00068-0.
Fox CH, Johnson FB, Whiting J, Roller PP. Formaldehyde fixation. J Histochem Cytochemistry. 1985;33(8):845–53. https://doi.org/10.1177/33.8.3894502
Rezende AS, Gutman TCF, Cunha KSG, Rodrigues FR, Lopes VGC. Frozen Section Analysis in Surgical Pathology: A Comprehensive Review. Med Clin Res. 2025;10(12):01–12. https://www.medclinrese.org/
Abeytunge S, Li Y, Larson B, Peterson G, Seltzer E, Toledo-Crow R, et al. Confocal microscopy with strip mosaicing for rapid imaging over large areas of excised tissue. J Biomed Opt SPIE-Intl Soc Opt Eng. 2013;18:061227. https://doi.org/10.1117/1.jbo.18.6.061227.
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inf Medknow Publications. 2016;7. https://doi.org/10.4103/2153-3539.186902.
Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2022;19:132–46. https://doi.org/10.1038/s41571-021-00560-7.
Lee C, Kim C, Park B. Review of Three-Dimensional Handheld Photoacoustic and Ultrasound Imaging Systems and Their Applications. Sensors. Multidisciplinary Digital Publishing Institute (MDPI); 2023. https://doi.org/10.3390/s23198149.
Park B, Han M, Kim H, Yoo J, Oh DK, Moon S, et al. Shear-Force Photoacoustic Microscopy: Toward Super-Resolution Near-Field Imaging. Laser Photon Rev John Wiley Sons Inc. 2022;16. https://doi.org/10.1002/lpor.202200296.
Kim J, Park B, Ha J, Steinberg I, Hooper SM, Jeong C, et al. Multiparametric photoacoustic analysis of human thyroid cancers in vivo. Cancer Res Am Association Cancer Res Inc. 2021;81:4849–60. https://doi.org/10.1158/0008-5472.CAN-20-3334.
Park B, Park S, Kim J, Kim C. Listening to drug delivery and responses via photoacoustic imaging. Adv Drug Deliv Rev Elsevier B V. 2022. https://doi.org/10.1016/j.addr.2022.114235.
Park E, Kim D, Ha M, Kim D, Kim C. A comprehensive review of high-performance photoacoustic microscopy systems. Photoacoustics. Elsevier GmbH; 2025. https://doi.org/10.1016/j.pacs.2025.100739.
Park B, Oh D, Kim J, Kim C. Functional photoacoustic imaging: from nano- and micro- to macro-scale. Nano Converg Korea Nano Technol Res Soc. 2023. https://doi.org/10.1186/s40580-023-00377-3.
Choi W, Park B, Choi S, Oh D, Kim J, Kim C. Recent Advances in Contrast-Enhanced Photoacoustic Imaging: Overcoming the Physical and Practical Challenges. Chem. Rev. American Chemical Society; 2023. pp. 7379–419. https://doi.org/10.1021/acs.chemrev.2c00627
Ahn J, Lee J, Kim K, Seong Bae J, Kwon Jung C, Kim M, A P, P L I E D S C I E N C E, S A N D E N G I N E E R. I N G Smarter biopsy decisions in thyroid nodules via dual-modal photoacoustic and ultrasound imaging [Internet]. Sci. Adv. 2025. https://www.science.org
Kang L, Li X, Zhang Y, Wong TTW. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining. Photoacoustics Elsevier GmbH. 2022;25. https://doi.org/10.1016/j.pacs.2021.100308.
Cao R, Nelson SD, Davis S, Liang Y, Luo Y, Zhang Y, et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat Biomed Eng Nat Res. 2023;7:124–34. https://doi.org/10.1038/s41551-022-00940-z.
Yoon C, Park E, Misra S, Kim JY, Baik JW, Kim KG, et al. Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens. Light Sci Appl Springer Nat. 2024;13. https://doi.org/10.1038/s41377-024-01554-7.
Park B, Cao R, Luo Y, Liu C, Zeng Y, Zhang Y, Zhou Q, Davis S, D’Apuzzo M, Wang LV. Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology. Science Advances. 2025 Nov 21;11(47):eadz1820. https://doi.org/10.1126/sciadv.adz1820
Martell MT, Haven NJM, Cikaluk BD, Restall BS, McAlister EA, Mittal R, et al. Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy. Nat Commun Nat Res. 2023;14. https://doi.org/10.1038/s41467-023-41574-2.
Boktor M, Ecclestone BR, Pekar V, Dinakaran D, Mackey JR, Fieguth P, et al. Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS). Sci Rep Nat Res. 2022;12. https://doi.org/10.1038/s41598-022-14042-y.
Kaza N, Ojaghi A, Robles FE. Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis. BME Front. American Association for the Advancement of Science; 2022;2022. https://doi.org/10.34133/2022/9853606
Yang X, Bai B, Zhang Y, Li Y, De Haan K, Liu T, et al. Virtual Stain Transfer in Histology via Cascaded Deep Neural Networks. ACS Photonics Am Chem Soc. 2022;9:3134–43. https://doi.org/10.1021/acsphotonics.2c00932.
Ghahremani P, Li Y, Kaufman A, Vanguri R, Greenwald N, Angelo M, et al. Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification. Nat Mach Intell Nat Res. 2022;4:401–12. https://doi.org/10.1038/s42256-022-00471-x.
Ho MM, Dubey S, Chong Y, Knudsen B, Tasdizen T. F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation. Proceedings – 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025. Institute of Electrical and Electronics Engineers Inc.; 2025. pp. 4382–91. https://doi.org/10.1109/WACV61041.2025.00430
Gadermayr M, Tschuchnig M, Stangassinger LM, Kreutzer C, Couillard-Despres S, Oostingh GJ, et al. Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization. Computer Methods and Programs in Biomedicine Update. Elsevier B.V.; 2023. p. 3. https://doi.org/10.1016/j.cmpbup.2023.100092.
Levy JJ, Azizgolshani N, Andersen MJ, Suriawinata A, Liu X, Lisovsky M, et al. A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies. Mod Pathol Springer Nat. 2021;34:808–22. https://doi.org/10.1038/s41379-020-00718-1.
Comments (0)