{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:03:24Z","timestamp":1775793804576,"version":"3.50.1"},"reference-count":116,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["NGF.1609.241.018"],"award-info":[{"award-number":["NGF.1609.241.018"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the state-of-the-art in virtual staining for breast cancer biomarkers (HER2, PgR, ER and Ki-67) and benchmarks several models on public datasets. It serves as a resource for researchers and clinicians interested in applying or developing virtual staining techniques.<\/jats:p>","DOI":"10.1038\/s41746-025-01741-9","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T09:03:47Z","timestamp":1751447027000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["H&amp;E to IHC virtual staining methods in breast cancer: an overview and benchmarking"],"prefix":"10.1038","volume":"8","author":[{"given":"Pascal","family":"Kl\u00f6ckner","sequence":"first","affiliation":[]},{"given":"Jos\u00e9","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Diana","family":"Montezuma","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Fraga","sequence":"additional","affiliation":[]},{"given":"Hugo M.","family":"Horlings","sequence":"additional","affiliation":[]},{"given":"Jaime S.","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Sara P.","family":"Oliveira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"1741_CR1","doi-asserted-by":"crossref","unstructured":"Funkhouser, W. K. Pathology: the clinical description of human disease. In Essential Concepts in Molecular Pathology (Second Edition), 177\u2013190 (2020).","DOI":"10.1016\/B978-0-12-813257-9.00011-5"},{"key":"1741_CR2","doi-asserted-by":"crossref","unstructured":"Magaki, S., Hojat, S. A., Wei, B., So, A. & Yong, W. H. An introduction to the performance of immunohistochemistry. In Methods in Molecular Biology, 289\u2013298 (Springer, 2019).","DOI":"10.1007\/978-1-4939-8935-5_25"},{"key":"1741_CR3","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","volume":"61","author":"M Veta","year":"2014","unstructured":"Veta, M., Pluim, J. P. W., van Diest, P. J. & Viergever, M. A. Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61, 1400\u20131411 (2014).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"1741_CR4","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1002\/path.5331","volume":"249","author":"E Abels","year":"2019","unstructured":"Abels, E. et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J. Pathol. 249, 286\u2013294 (2019).","journal-title":"J. Pathol."},{"key":"1741_CR5","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1111\/joim.13030","volume":"288","author":"B Acs","year":"2020","unstructured":"Acs, B., Rantalainen, M. & Hartman, J. Artificial intelligence as the next step towards precision pathology. J. Intern. Med. 288, 62\u201381 (2020).","journal-title":"J. Intern. Med."},{"key":"1741_CR6","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1016\/j.tibtech.2024.02.009","volume":"42","author":"L Latonen","year":"2024","unstructured":"Latonen, L., Koivukoski, S., Khan, U. & Ruusuvuori, P. Virtual staining for histology by deep learning. Trends Biotechnol. 42, 1177\u20131191 (2024).","journal-title":"Trends Biotechnol."},{"key":"1741_CR7","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1080\/14737159.2022.2153040","volume":"22","author":"N Pillar","year":"2022","unstructured":"Pillar, N. & Ozcan, A. Virtual tissue staining in pathology using machine learning. Expert Rev. Mol. Diagn. 22, 987\u2013989 (2022).","journal-title":"Expert Rev. Mol. Diagn."},{"key":"1741_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41377-023-01104-7","volume":"12","author":"B Bai","year":"2023","unstructured":"Bai, B. et al. Deep learning-enabled virtual histological staining of biological samples. Light Sci. Appl. 12, 57 (2023).","journal-title":"Light Sci. Appl."},{"key":"1741_CR9","doi-asserted-by":"publisher","unstructured":"Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.1312.6114 (2022).","DOI":"10.48550\/arXiv.1312.6114"},{"key":"1741_CR10","unstructured":"Goodfellow, I. et al. Generative Adversarial Nets. In Advances in Neural Information Processing Systems\u2014NIPS, 27, 2672\u20132680 (2014)."},{"key":"1741_CR11","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In IEEE International Conference on Computer Vision\u2014ICCV, 2242\u20132251 (2017).","DOI":"10.1109\/ICCV.2017.244"},{"key":"1741_CR12","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1016\/S0140-6736(22)00184-2","volume":"399","author":"CE Coles","year":"2022","unstructured":"Coles, C. E. et al. The Lancet Breast Cancer Commission: tackling a global health, gender, and equity challenge. Lancet 399, 1101\u20131103 (2022).","journal-title":"Lancet"},{"key":"1741_CR13","unstructured":"World Health Organization. Breast Cancer. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/breast-cancer (2024)."},{"key":"1741_CR14","unstructured":"World Health Organization. Global Breast Cancer Initiative Implementation Framework: assessing, strengthening and scaling-up of services for the early detection and management of breast cancer. https:\/\/iris.who.int\/bitstream\/handle\/10665\/365784\/9789240067134-eng.pdf (2023)."},{"key":"1741_CR15","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.bbacli.2017.01.001","volume":"7","author":"KE Lukong","year":"2017","unstructured":"Lukong, K. E. Understanding breast cancer\u2014-the long and winding road. BBA Clin. 7, 64\u201377 (2017).","journal-title":"BBA Clin."},{"key":"1741_CR16","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/nature11412","volume":"490","author":"The Cancer Genome Atlas Network.","year":"2012","unstructured":"The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61\u201370 (2012).","journal-title":"Nature"},{"key":"1741_CR17","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1093\/annonc\/mdt303","volume":"24","author":"A Goldhirsch","year":"2013","unstructured":"Goldhirsch, A. et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann. Oncol. 24, 2206\u20132223 (2013).","journal-title":"Ann. Oncol."},{"key":"1741_CR18","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1016\/j.ajpath.2017.04.022","volume":"187","author":"HG Russnes","year":"2017","unstructured":"Russnes, H. G., Lingj\u00e6rde, O. C., B\u00f8rresen-Dale, A.-L. & Caldas, C. Breast cancer molecular stratification: from intrinsic subtypes to integrative clusters. Am. J. Pathol. 187, 2152\u20132162 (2017).","journal-title":"Am. J. Pathol."},{"key":"1741_CR19","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1093\/annonc\/mdn675","volume":"20","author":"A Spitale","year":"2009","unstructured":"Spitale, A., Mazzola, P., Soldini, D., Mazzucchelli, L. & Bordoni, A. Breast cancer classification according to immunohistochemical markers: clinicopathologic features and short-term survival analysis in a population-based study from the South of Switzerland. Ann. Oncol. 20, 628\u2013635 (2009).","journal-title":"Ann. Oncol."},{"key":"1741_CR20","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1093\/annonc\/mdv221","volume":"26","author":"AS Coates","year":"2015","unstructured":"Coates, A. S. et al. Tailoring therapies\u2014improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann. Oncol. 26, 1533\u20131546 (2015).","journal-title":"Ann. Oncol."},{"key":"1741_CR21","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1093\/annonc\/mdx308","volume":"28","author":"G Curigliano","year":"2017","unstructured":"Curigliano, G. et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann. Oncol. 28, 1700\u20131712 (2017).","journal-title":"Ann. Oncol."},{"key":"1741_CR22","unstructured":"Collins, L. Tumors of the Mammary Gland. AFIP Atlas of Tumor and Non-Tumor Pathology (American Registry of Pathology, 2024)."},{"key":"1741_CR23","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1136\/jclinpath-2014-202571","volume":"68","author":"EA Rakha","year":"2015","unstructured":"Rakha, E. A. et al. Updated UK Recommendations for HER2 assessment in breast cancer. J. Clin. Pathol. 68, 93\u201399 (2015).","journal-title":"J. Clin. Pathol."},{"key":"1741_CR24","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1200\/JCO.19.02309","volume":"38","author":"KH Allison","year":"2020","unstructured":"Allison, K. H. et al. Estrogen and progesterone receptor testing in breast cancer: ASCO\/CAP guideline update. J. Clin. Oncol. 38, 1346\u20131366 (2020).","journal-title":"J. Clin. Oncol."},{"key":"1741_CR25","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1159\/000525092","volume":"89","author":"EA Rakha","year":"2022","unstructured":"Rakha, E. A. et al. Assessment of predictive biomarkers in breast cancer: challenges and updates. Pathobiology 89, 263\u2013277 (2022).","journal-title":"Pathobiology"},{"key":"1741_CR26","doi-asserted-by":"publisher","first-page":"3867","DOI":"10.1200\/JCO.22.02864","volume":"41","author":"AC Wolff","year":"2023","unstructured":"Wolff, A. C. et al. Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology\u2013College of American Pathologists Guideline Update. J. Clin. Oncol. 41, 3867\u20133872 (2023).","journal-title":"J. Clin. Oncol."},{"key":"1741_CR27","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3389\/fmed.2018.00248","volume":"5","author":"AF Vieira","year":"2018","unstructured":"Vieira, A. F. & Schmitt, F. An update on breast cancer multigene prognostic tests-emergent clinical biomarkers. Front. Med. 5, 248 (2018).","journal-title":"Front. Med."},{"key":"1741_CR28","doi-asserted-by":"publisher","first-page":"8716","DOI":"10.3390\/ijms23158716","volume":"23","author":"R Erber","year":"2022","unstructured":"Erber, R. et al. Molecular subtyping of invasive breast cancer using a PAM50-based multigene expression test-comparison with molecular-like subtyping by tumor grade\/immunohistochemistry and influence on oncologist\u2019s decision on systemic therapy in a real-world setting. Int. J. Mol. Sci. 23, 8716 (2022).","journal-title":"Int. J. Mol. Sci."},{"key":"1741_CR29","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.3390\/cancers14143469","volume":"14","author":"SM Hacking","year":"2022","unstructured":"Hacking, S. M., Yakirevich, E. & Wang, Y. From immunohistochemistry to new digital ecosystems: a state-of-the-art biomarker review for precision breast cancer medicine. Cancers 14, 3469 (2022).","journal-title":"Cancers"},{"key":"1741_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1111\/his.14786","volume":"82","author":"EA Rakha","year":"2023","unstructured":"Rakha, E. A., Tse, G. M. & Quinn, C. M. An update on the pathological classification of breast cancer. Histopathology 82, 5\u201316 (2023).","journal-title":"Histopathology"},{"key":"1741_CR31","doi-asserted-by":"publisher","first-page":"19","DOI":"10.4274\/ejbh.galenos.2023.2023-6-3","volume":"20","author":"C Karaali","year":"2024","unstructured":"Karaali, C. et al. The clinical and pathological characteristics that differentiate cases with \u201cLow Estrogen Receptor Expression\u201d from triple-negative breast cancer. Eur. J. Breast Health 20, 19 (2024).","journal-title":"Eur. J. Breast Health"},{"key":"1741_CR32","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1200\/JCO.2005.05.203","volume":"23","author":"R Colomer","year":"2005","unstructured":"Colomer, R. et al. It is not time to stop progesterone receptor testing in breast cancer. J. Clin. Oncol. 23, 3868\u20133869 (2005).","journal-title":"J. Clin. Oncol."},{"key":"1741_CR33","doi-asserted-by":"publisher","first-page":"30","DOI":"10.21037\/tbcr-22-48","volume":"3","author":"J Li","year":"2022","unstructured":"Li, J. et al. Expert consensus on the clinical diagnosis and targeted therapy of HER2 breast cancer. Transl. Breast Cancer Res. 3, 30 (2022).","journal-title":"Transl. Breast Cancer Res."},{"key":"1741_CR34","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1093\/jnci\/djaa201","volume":"113","author":"TO Nielsen","year":"2021","unstructured":"Nielsen, T. O. et al. Assessment of Ki67 in breast cancer: updated recommendations from the international Ki67 in breast cancer working group. J. Natl Cancer Inst. 113, 808\u2013819 (2021).","journal-title":"J. Natl Cancer Inst."},{"key":"1741_CR35","unstructured":"Ramesh, A. et al. Zero-shot text-to-image generation. In Proceedings of the 38th International Conference on Machine Learning - ICML 2021, 139, 8821-8831 (2021)."},{"key":"1741_CR36","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 10674\u201310685 (2022).","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"1741_CR37","doi-asserted-by":"publisher","unstructured":"Mirza, M. & Osindero, S. Conditional generative adversarial nets. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.1411.1784 (2014).","DOI":"10.48550\/arXiv.1411.1784"},{"key":"1741_CR38","doi-asserted-by":"crossref","unstructured":"Park, T., Efros, A. A., Zhang, R. & Zhu, J.-Y. Contrastive learning for unpaired image-to-image translation. In European Conference on Computer Vision - ECCV 2020, 319\u2013345 (2020).","DOI":"10.1007\/978-3-030-58545-7_19"},{"key":"1741_CR39","unstructured":"Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N. & Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning - ICML 2015, 37, 2256-2265 (2015)."},{"key":"1741_CR40","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-Image Translation with Conditional Adversarial Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR, 5967\u20135976 (2017).","DOI":"10.1109\/CVPR.2017.632"},{"key":"1741_CR41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, 234\u2013241 (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1741_CR42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition \u2013 CVPR, 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"1741_CR43","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR, 248\u2013255 (2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1741_CR44","doi-asserted-by":"crossref","unstructured":"Li, F., Hu, Z., Chen, W. & Kak, A. Adaptive supervised PatchNCE loss for learning H&E-to-IHC stain translation with inconsistent groundtruth image pairs. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI, 632\u2013641 (2023).","DOI":"10.1007\/978-3-031-43987-2_61"},{"key":"1741_CR45","doi-asserted-by":"crossref","unstructured":"Tomczak, J. M.-Deep Generative Modeling (Springer International Publishing, 2024).","DOI":"10.1007\/978-3-031-64087-2"},{"key":"1741_CR46","unstructured":"Ho, J., Jain, A., & Abbeel, P. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems - NeurIPS, 33, 6840-6851 (2020)."},{"key":"1741_CR47","first-page":"4713","volume":"45","author":"C Saharia","year":"2023","unstructured":"Saharia, C. et al. Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45, 4713\u20134726 (2023).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1741_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, Y. et al. Inversion-based Style Transfer with Diffusion Models. In 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 10146\u201310156 (2023).","DOI":"10.1109\/CVPR52729.2023.00978"},{"key":"1741_CR49","unstructured":"Dhariwal, P. & Nichol, A. Diffusion models beat GANs on image synthesis. In Proceedings of the 35th International Conference on Neural Information Processing Systems\u2014NIPS, 8780\u20138794 (2021)."},{"key":"1741_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-39278-0","volume":"13","author":"G M\u00fcller-Franzes","year":"2023","unstructured":"M\u00fcller-Franzes, G. et al. A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci. Rep. 13, 12098 (2023).","journal-title":"Sci. Rep."},{"key":"1741_CR51","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H. & Simoncelli, E. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004).","journal-title":"IEEE Trans. Image Process."},{"key":"1741_CR52","unstructured":"Wang, Z., Simoncelli, E. & Bovik, A. Multiscale structural similarity for image quality assessment. In The 37th Asilomar Conference on Signals, Systems & Computers\u2014ACSSC, 1398\u20131402 (2003)."},{"key":"1741_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. A., Shechtman, E. & Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 586\u2013595 (2018).","DOI":"10.1109\/CVPR.2018.00068"},{"key":"1741_CR54","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. & Hochreiter, S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems\u2014NIPS, 6629-6640 (2017)."},{"key":"1741_CR55","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR, 2818\u20132826 (2016).","DOI":"10.1109\/CVPR.2016.308"},{"key":"1741_CR56","unstructured":"Bi\u0144kowski, M., Sutherland, D. J., Arbel, M. & Gretton, A. Demystifying MMD GANs. In International Conference on Learning Representations\u2014ICLR (2018)."},{"key":"1741_CR57","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok, A. C. et al. Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23, 291\u2013299 (2001).","journal-title":"Anal. Quant. Cytol. Histol."},{"key":"1741_CR58","doi-asserted-by":"publisher","first-page":"100354","DOI":"10.1016\/j.jpi.2023.100354","volume":"15","author":"F Martino","year":"2024","unstructured":"Martino, F. et al. A deep learning model to predict ki-67 positivity in oral squamous cell carcinoma. J. Pathol. Inform. 15, 100354 (2024).","journal-title":"J. Pathol. Inform."},{"key":"1741_CR59","doi-asserted-by":"crossref","unstructured":"Peng, Q. et al. Advancing H&E-to-IHC virtual staining with task-specific domain knowledge for HER2 scoring. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024, 3\u201313 (2024).","DOI":"10.1007\/978-3-031-72083-3_1"},{"key":"1741_CR60","doi-asserted-by":"crossref","unstructured":"Zeng, B. et al. Semi-supervised PR Virtual Staining for Breast Histopathological Images. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022, 232\u2013241 (2022).","DOI":"10.1007\/978-3-031-16434-7_23"},{"key":"1741_CR61","doi-asserted-by":"publisher","first-page":"101544","DOI":"10.1016\/j.media.2019.101544","volume":"58","author":"D Tellez","year":"2019","unstructured":"Tellez, D. et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med. Image Anal. 58, 101544 (2019).","journal-title":"Med. Image Anal."},{"key":"1741_CR62","doi-asserted-by":"crossref","unstructured":"Liu, S. et al. BCI: breast cancer immunohistochemical image generation through pyramid Pix2pix. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops\u2014CVPRW, 1814\u20131823 (2022).","DOI":"10.1109\/CVPRW56347.2022.00198"},{"key":"1741_CR63","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2010","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196\u2013205 (2010).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1741_CR64","doi-asserted-by":"publisher","unstructured":"Akbarnejad, A., Ray, N., Barnes, P. J. & Bigras, G. Predicting Ki67, ER, PR, and HER2 statuses from H&E-stained breast cancer images. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.2308.01982 (2023).","DOI":"10.48550\/arXiv.2308.01982"},{"key":"1741_CR65","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1111\/his.13333","volume":"72","author":"T Qaiser","year":"2018","unstructured":"Qaiser, T. et al. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 72, 227\u2013238 (2018).","journal-title":"Histopathology"},{"key":"1741_CR66","unstructured":"Academia and Industry Collaboration for Digital Pathology. AIDPATH DB. https:\/\/mitel.dimi.uniud.it\/aidpath-db\/app\/login.php (2017)."},{"key":"1741_CR67","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.compmedimag.2017.04.005","volume":"61","author":"D Pilutti","year":"2017","unstructured":"Pilutti, D. et al. An adaptive positivity thresholding method for automated Ki67 hotspot detection (AKHoD) in breast cancer biopsies. Comput. Med. Imaging Graph. 61, 28\u201334 (2017).","journal-title":"Comput. Med. Imaging Graph."},{"key":"1741_CR68","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1038\/s41597-023-02422-6","volume":"10","author":"P Weitz","year":"2023","unstructured":"Weitz, P. et al. A multi-stain breast cancer histological whole-slide-image data set from routine diagnostics. Sci. Data 10, 562 (2023).","journal-title":"Sci. Data"},{"key":"1741_CR69","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/TMI.2020.2986331","volume":"39","author":"J Borovec","year":"2020","unstructured":"Borovec, J. et al. ANHIR: automatic non-rigid histological image registration challenge. IEEE Trans. Med. Imaging 39, 3042\u20133052 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1741_CR70","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1109\/TMI.2021.3069874","volume":"40","author":"S Liu","year":"2021","unstructured":"Liu, S. et al. Unpaired stain transfer using pathology-consistent constrained generative adversarial networks. IEEE Trans. Med. Imaging 40, 1977\u20131989 (2021).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1741_CR71","doi-asserted-by":"publisher","first-page":"105799","DOI":"10.1016\/j.cmpb.2020.105799","volume":"198","author":"M Wodzinski","year":"2021","unstructured":"Wodzinski, M. & M\u00fcller, H. DeepHistReg: unsupervised deep learning registration framework for differently stained histology samples. Comput. Methods Prog. Biomed. 198, 105799 (2021).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"1741_CR72","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR, 2921\u20132929 (2016).","DOI":"10.1109\/CVPR.2016.319"},{"key":"1741_CR73","unstructured":"Kim, J., Kim, M., Kang, H. & Lee, K. H. U-GAT-IT: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. In International Conference on Learning Representations\u2014ICLR (2020)."},{"key":"1741_CR74","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.-Y., Belongie, S. & Kautz, J. Multimodal unsupervised image-to-image translation. In European Conference on Computer Vision\u2014ECCV 2018, 179\u2013196 (2018).","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"1741_CR75","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1109\/TMI.2023.3337253","volume":"43","author":"J Ma","year":"2024","unstructured":"Ma, J. & Chen, H. Efficient supervised pretraining of swin-transformer for virtual staining of microscopy images. IEEE Trans. Med. Imaging 43, 1388\u20131399 (2024).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1741_CR76","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. In 2021 IEEE\/CVF International Conference on Computer Vision\u2014ICCV, 9992\u201310002 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1741_CR77","doi-asserted-by":"crossref","unstructured":"He, K. et al. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 16000\u201316009 (2022).","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1741_CR78","doi-asserted-by":"crossref","unstructured":"Xie, Z. et al. SimMIM: a simple framework for masked image modeling. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 9653\u20139663 (2022).","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"1741_CR79","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cell.2018.03.040","volume":"173","author":"EM Christiansen","year":"2018","unstructured":"Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792\u2013803 (2018).","journal-title":"Cell"},{"key":"1741_CR80","doi-asserted-by":"crossref","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G. & Barlaud, M. Two deterministic half-quadratic regularization algorithms for computed imaging. In Proceedings of 1st IEEE International Conference on Image Processing\u2014ICIP, 168\u2013172 (1994).","DOI":"10.1109\/ICIP.1994.413553"},{"key":"1741_CR81","doi-asserted-by":"crossref","unstructured":"Baldeon-Calisto, M. et al. DeepSIT: deeply supervised framework for image translation on breast cancer analysis. In IEEE 13th International Conference on Pattern Recognition Systems\u2014ICPRS), 1\u20137 (2023).","DOI":"10.1109\/ICPRS58416.2023.10178999"},{"key":"1741_CR82","doi-asserted-by":"crossref","unstructured":"Wang, T.-C. et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 8798\u20138807 (2018).","DOI":"10.1109\/CVPR.2018.00917"},{"key":"1741_CR83","unstructured":"Liu, S. et al. Breast Cancer Immunohistochemical Image Generation Challenge. bci.grand-challenge.org (2024)."},{"key":"1741_CR84","doi-asserted-by":"publisher","first-page":"108046","DOI":"10.1016\/j.compbiomed.2024.108046","volume":"170","author":"Y Ma","year":"2024","unstructured":"Ma, Y. et al. Dsff-gan: A novel stain transfer network for generating immunohistochemical image of endometrial cancer. Comp. Biol. Med. 170, 108046 (2024).","journal-title":"Comp. Biol. Med."},{"key":"1741_CR85","first-page":"2567","volume":"44","author":"K Ding","year":"2022","unstructured":"Ding, K., Ma, K., Wang, S. & Simoncelli, E. P. Image quality assessment: unifying structure and texture similarity. IEEE Trans. Pattern Anal. Mach. Intell. 44, 2567\u20132581 (2022).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1741_CR86","doi-asserted-by":"crossref","unstructured":"Wei, L., Hua, S., Zhang, S. & Zhang, X. DeReStainer: H&E to IHC pathological image translation via decoupled staining channels. In -(eds Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Mehrof, D. & Yuan, Y.) Lecture Notes in Computer Science, Deep Generative Models. DGM4MICCAI 2024, 1\u201310 (Springer, 2025).","DOI":"10.1007\/978-3-031-72744-3_1"},{"key":"1741_CR87","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Doll\u00e1r, P. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision\u2014ICCV, 2999\u20133007 (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"1741_CR88","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TIP.2024.3349866","volume":"33","author":"X Guan","year":"2024","unstructured":"Guan, X., Wang, Y., Lin, Y., Li, X. & Zhang, Y. Unsupervised multi-domain progressive stain transfer guided by style encoding dictionary. IEEE Trans. Image Process. 33, 767\u2013779 (2024).","journal-title":"IEEE Trans. Image Process."},{"key":"1741_CR89","doi-asserted-by":"publisher","first-page":"102520","DOI":"10.1016\/j.media.2022.102520","volume":"80","author":"R Zhang","year":"2022","unstructured":"Zhang, R. et al. MVFStain: multiple virtual functional stain histopathology images generation based on specific domain mapping. Med. Image Anal. 80, 102520 (2022).","journal-title":"Med. Image Anal."},{"key":"1741_CR90","unstructured":"Ustinova, E. & Lempitsky, V. Learning deep embeddings with histogram loss. In Proceedings of the 30th International Conference on Neural Information Processing Systems\u2014NIPS, 4177-4185 (2016)."},{"key":"1741_CR91","doi-asserted-by":"crossref","unstructured":"Qu, L. et al. Advancing H&E-to-IHC stain translation in breast cancer: a multi-magnification and attention-based approach. In 2024 IEEE International Conference on Cybernetics and Intelligent Systems and IEEE International Conference on Robotics, Automation and Mechatronics\u2014CIS-RAM, 441\u2013446 (2024).","DOI":"10.1109\/CIS-RAM61939.2024.10673328"},{"key":"1741_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Van Gool, L. & Timofte, R. Designing a practical degradation model for deep blind image super-resolution. In IEEE International Conference on Computer Vision, 4791\u20134800 (2021).","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"1741_CR93","doi-asserted-by":"crossref","unstructured":"Chen, F. et al. Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024, 384\u2013394 (2024).","DOI":"10.1007\/978-3-031-72083-3_36"},{"key":"1741_CR94","doi-asserted-by":"crossref","unstructured":"Li, Y., Guan, X., Wang, Y. & Zhang, Y. Exploiting supervision information in weakly paired images for IHC virtual staining. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024, 113\u2013122 (2024).","DOI":"10.1007\/978-3-031-72083-3_11"},{"key":"1741_CR95","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, Z., Yan, H., Xu, M. & Wang, G. Mix-domain contrastive learning for unpaired H&E-to-IHC stain translation. In 2024 IEEE International Conference on Image Processing\u2014ICIP, 2982\u20132988 (2024).","DOI":"10.1109\/ICIP51287.2024.10648270"},{"key":"1741_CR96","doi-asserted-by":"crossref","unstructured":"Zhang, W. et al. High-resolution medical image translation via e20614patch alignment-based bidirectional contrastive learning. In Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024, 178\u2013188 (2024).","DOI":"10.1007\/978-3-031-72083-3_17"},{"key":"1741_CR97","doi-asserted-by":"publisher","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.1409.1556 (2015).","DOI":"10.48550\/arXiv.1409.1556"},{"key":"1741_CR98","doi-asserted-by":"crossref","unstructured":"Li, J. et al. Virtual immunohistochemistry staining for histological images assisted by weakly-supervised learning. In 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 11259\u201311268 (2024).","DOI":"10.1109\/CVPR52733.2024.01070"},{"key":"1741_CR99","unstructured":"Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In 35th International Conference on Machine Learning\u2014ICML 2018, 3376\u20133391 (2018)."},{"key":"1741_CR100","doi-asserted-by":"crossref","unstructured":"Li, B., Xue, K., Liu, B. & Lai, Y.-K. BBDM: Image-to-image translation with brownian bridge diffusion models. In 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR, 1952\u20131961 (2023).","DOI":"10.1109\/CVPR52729.2023.00194"},{"key":"1741_CR101","doi-asserted-by":"publisher","first-page":"3634","DOI":"10.1109\/TMI.2024.3430825","volume":"43","author":"Y He","year":"2024","unstructured":"He, Y. et al. PST-Diff: achieving high-consistency stain transfer by diffusion models with pathological and structural constraints. IEEE Trans. Med. Imaging 43, 3634\u20133647 (2024).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1741_CR102","doi-asserted-by":"publisher","unstructured":"Su, X., Song, J., Meng, C. & Ermon, S. Dual diffusion implicit bridges for image-to-image translation. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.2203.08382 (2023).","DOI":"10.48550\/arXiv.2203.08382"},{"key":"1741_CR103","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, S., Jeong, Y., Gwon, Y. & Yoon, S. ILVR: conditioning method for denoising diffusion probabilistic models. In 2021 IEEE\/CVF International Conference on Computer Vision\u2014ICCV, 14347\u201314356 (2021).","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"1741_CR104","doi-asserted-by":"publisher","unstructured":"Li, Z. et al. His-MMDM: multi-domain and multi-omics translation of histopathology images with diffusion models. medRxiv https:\/\/doi.org\/10.1101\/2024.07.11.24310294 (2024).","DOI":"10.1101\/2024.07.11.24310294"},{"key":"1741_CR105","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1038\/s41551-022-00952-9","volume":"6","author":"KB Ozyoruk","year":"2022","unstructured":"Ozyoruk, K. B. et al. A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nat. Biomed. Eng. 6, 1407\u20131419 (2022).","journal-title":"Nat. Biomed. Eng."},{"key":"1741_CR106","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s43856-021-00013-3","volume":"1","author":"P Gamble","year":"2021","unstructured":"Gamble, P. et al. Determining breast cancer biomarker status and associated morphological features using deep learning. Comm. Med. 1, 14 (2021).","journal-title":"Comm. Med."},{"key":"1741_CR107","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1038\/s42256-024-00889-5","volume":"6","author":"P Pati","year":"2024","unstructured":"Pati, P. et al. Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nat. Mach. Intell. 6, 1077\u20131093 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"1741_CR108","doi-asserted-by":"publisher","first-page":"100255","DOI":"10.1016\/j.labinv.2023.100255","volume":"103","author":"A Waqas","year":"2023","unstructured":"Waqas, A. et al. Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models. Lab. Investig. 103, 100255 (2023).","journal-title":"Lab. Investig."},{"key":"1741_CR109","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1136\/jitc-2023-008645","volume":"12","author":"M Levin","year":"2024","unstructured":"Levin, M. et al. Multi-modal spatial analysis of classic Hodgkin lymphoma microenvironments utilizing multiplex immunofluorescence and virtual staining. J. Immunother. Cancer 12, 1195 (2024).","journal-title":"J. Immunother. Cancer"},{"key":"1741_CR110","doi-asserted-by":"crossref","unstructured":"Duan, G., Cao, Y., Guo, W., Cui, L. & Liu, Z. A virtual staining method for immunohistochemical images of breast cancer. In 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics\u2014CISP-BMEI), 1\u20135 (2023).","DOI":"10.1109\/CISP-BMEI60920.2023.10373388"},{"key":"1741_CR111","doi-asserted-by":"crossref","unstructured":"Huang, S. et al. Tc-cyclegan: improved cyclegan with texture constraints for virtual staining of pathological images. In Proceedings of the 3rd International Conference on Bioinformatics and Intelligent Computing\u2014BIC\u201923, 147\u2013152 (2023).","DOI":"10.1145\/3592686.3592713"},{"key":"1741_CR112","doi-asserted-by":"publisher","first-page":"e20614","DOI":"10.1016\/j.heliyon.2023.e20614","volume":"9","author":"L Liu","year":"2023","unstructured":"Liu, L. et al. MGGAN: a multi-generator generative adversarial network for breast cancer immunohistochemical image generation. Heliyon 9, e20614 (2023).","journal-title":"Heliyon"},{"key":"1741_CR113","doi-asserted-by":"publisher","first-page":"e37902","DOI":"10.1016\/j.heliyon.2024.e37902","volume":"10","author":"S Wu","year":"2024","unstructured":"Wu, S. & Xu, S. HcGAN: harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E. Heliyon 10, e37902 (2024).","journal-title":"Heliyon"},{"key":"1741_CR114","doi-asserted-by":"crossref","unstructured":"Hu, J. et al. ULViT-GAN: Advancing stain transfer in H&E and IHC pathology images with a UNet-like vision-transformer GAN. In Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering\u2014CAICE\u201924, 524-528 (2024).","DOI":"10.1145\/3672758.3672843"},{"key":"1741_CR115","doi-asserted-by":"crossref","unstructured":"Jia, Y., Duan, G., Song, Y., Ye, L. & Liu, Z. DTNet: Dual-encoder generative adversarial network for generating breast cancer immunohistochemical images. In 2024 5th International Conference on Computer Vision, Image and Deep Learning\u2014CVIDL, 937\u2013942 (2024).","DOI":"10.1109\/CVIDL62147.2024.10603639"},{"key":"1741_CR116","doi-asserted-by":"publisher","first-page":"047501","DOI":"10.1117\/1.JMI.11.4.047501","volume":"11","author":"C Ji","year":"2024","unstructured":"Ji, C. et al. Transformation from hematoxylin-and-eosin staining to Ki-67 immunohistochemistry digital staining images using deep learning: experimental validation on the labeling index. J. Med. Imaging 11, 047501 (2024).","journal-title":"J. Med. Imaging"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01741-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01741-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01741-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T09:04:32Z","timestamp":1751447072000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01741-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":116,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1741"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01741-9","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"27 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"384"}}