{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:03:38Z","timestamp":1784235818166,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02043-6","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:10:45Z","timestamp":1784232645000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Translation of H&amp;E to IHC with Robust Tumor-Infiltrating Lymphocytes Quantification Using Deep Generative Models: A Feasibility Study"],"prefix":"10.1007","author":[{"given":"Doa","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiseon","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee Jin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Seon","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miseon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byung-Kwan","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uiree","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"issue":"3","key":"2043_CR1","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S1470-2045(00)00254-0","volume":"2","author":"TJ Key","year":"2001","unstructured":"Key, T.J., P.K. Verkasalo, and E. Banks, Epidemiology of breast cancer. The lancet oncology, 2001. 2(3): p. 133-140.","journal-title":"The lancet oncology"},{"issue":"2","key":"2043_CR2","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1093\/annonc\/mdu450","volume":"26","author":"R Salgado","year":"2015","unstructured":"Salgado, R., et al., The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Annals of oncology, 2015. 26(2): p. 259-271.","journal-title":"Annals of oncology"},{"issue":"1","key":"2043_CR3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s41523-020-0156-0","volume":"6","author":"Z Kos","year":"2020","unstructured":"Kos, Z., et al., Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer. NPJ breast cancer, 2020. 6(1): p. 17.","journal-title":"NPJ breast cancer"},{"issue":"14","key":"2043_CR4","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.3390\/diagnostics13142339","volume":"13","author":"A Kazemi","year":"2023","unstructured":"Kazemi, A., et al., Assessment of the tumor\u2013stroma ratio and tumor-infiltrating lymphocytes in colorectal cancer: Inter-observer agreement evaluation. Diagnostics, 2023. 13(14): p. 2339.","journal-title":"Diagnostics"},{"key":"2043_CR5","doi-asserted-by":"publisher","unstructured":"Vidal JM, Tsiknakis N, Staaf J, Bosch A, Ehinger A, Nimeus E, Salgado R, Bai Y, Rimm DL, Hartman J, Acs B.,\u00a0The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?\u00a0 EClinicalMedicine. 2024 Nov 15. 78:102928. https:\/\/doi.org\/10.1016\/j.eclinm.2024.102928.","DOI":"10.1016\/j.eclinm.2024.102928"},{"issue":"1","key":"2043_CR6","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1186\/s12885-024-12962-8","volume":"24","author":"NB Baharun","year":"2024","unstructured":"Baharun, N.B., et al., Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review. BMC cancer, 2024. 24(1): p. 1202.","journal-title":"BMC cancer"},{"key":"2043_CR7","doi-asserted-by":"crossref","unstructured":"Shephard, A.J., Jahanifar, M., Wang, R., Dawood, M., Graham, S., Sidlauskas, K., Khurram, S.A., Rajpoot, N.M., & Raza, S.E., An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2023. 1-5.","DOI":"10.1109\/ISBI56570.2024.10635302"},{"issue":"3","key":"2043_CR8","doi-asserted-by":"publisher","first-page":"382","DOI":"10.5306\/wjco.v5.i3.382","volume":"5","author":"DC Zaha","year":"2014","unstructured":"Zaha, D.C., Significance of immunohistochemistry in breast cancer. World journal of clinical oncology, 2014. 5(3): p. 382.","journal-title":"World journal of clinical oncology"},{"key":"2043_CR9","doi-asserted-by":"publisher","first-page":"1404640","DOI":"10.3389\/fimmu.2024.1404640","volume":"15","author":"AB Azam","year":"2024","unstructured":"Azam, A.B., et al., Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy. Frontiers in Immunology, 2024. 15: p. 1404640.","journal-title":"Frontiers in Immunology"},{"issue":"4","key":"2043_CR10","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1002\/cac2.12023","volume":"40","author":"WCC Tan","year":"2020","unstructured":"Tan, W.C.C., et al., Overview of multiplex immunohistochemistry\/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Communications, 2020. 40(4): p. 135-153.","journal-title":"Cancer Communications"},{"key":"2043_CR11","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 International Conference on Medical Image Computing and Computer-Assisted Intervention 2023, October. (pp. 632-641). Cham: Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-43987-2_61"},{"key":"2043_CR12","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhu, C., Xu, F., Jia, X., Shi, Z., & Jin, M., Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022. (pp. 1815-1824).","DOI":"10.1109\/CVPRW56347.2022.00198"},{"key":"2043_CR13","doi-asserted-by":"crossref","unstructured":"Qu, L., Zhang, C., Li, G., Zheng, H., Peng, C., & He, W., 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 (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM) 2024, August. (pp. 441-446). IEEE.","DOI":"10.1109\/CIS-RAM61939.2024.10673328"},{"key":"2043_CR14","doi-asserted-by":"publisher","unstructured":"Wu, Shuying & Xu, Shiwei., HcGAN: Harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E. Heliyon, 2024.\u00a010. e37902. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e37902.","DOI":"10.1016\/j.heliyon.2024.e37902"},{"key":"2043_CR15","unstructured":"Rehmat, S., Rehman, H. U., Kang, B. G., Ayouni, S., & Nam, Y., Transforming H&E images into IHC: A Variance-Penalized GAN for Precision Oncology.\u00a02025.\u00a0arXiv preprint arXiv:2506.18371."},{"key":"2043_CR16","volume-title":"MICCAI Workshop on Deep Generative Models","author":"L Wei","year":"2024","unstructured":"Wei, L., et al. DeReStainer: H&E to IHC pathological image translation via decoupled staining channels. in MICCAI Workshop on Deep Generative Models. 2024. Springer."},{"issue":"3","key":"2043_CR17","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/bcr3658","volume":"16","author":"LG Martelotto","year":"2014","unstructured":"Martelotto, L.G., et al., Breast cancer intra-tumor heterogeneity. Breast Cancer Research, 2014. 16(3): p. 210.","journal-title":"Breast Cancer Research"},{"issue":"19","key":"2043_CR18","doi-asserted-by":"publisher","first-page":"4605","DOI":"10.1093\/bioinformatics\/btac558","volume":"38","author":"H Duanmu","year":"2022","unstructured":"Duanmu, H., et al., A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images. Bioinformatics, 2022. 38(19): p. 4605-4612.","journal-title":"Bioinformatics"},{"key":"2043_CR19","unstructured":"Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001 Aug. 23(4):291-9."},{"key":"2043_CR20","unstructured":"Litjens, G. (2015), ASAP - Automated Slide Analysis Platform.[Computer software]. Github. https:\/\/github.com\/computationalpathologygroup\/asap."},{"issue":"2","key":"2043_CR21","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G., Distinctive image features from scale-invariant keypoints. International journal of computer vision, 2004. 60(2): p. 91-110.","journal-title":"International journal of computer vision"},{"issue":"6","key":"2043_CR22","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A. and R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981. 24(6): p. 381-395.","journal-title":"Communications of the ACM"},{"issue":"2","key":"2043_CR23","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1001\/jamaoncol.2024.5356","volume":"11","author":"M Rakaee","year":"2025","unstructured":"Rakaee, M., et al., Deep learning model for predicting immunotherapy response in advanced Non\u2212 Small cell lung cancer. JAMA oncology, 2025. 11(2): p. 109-118.","journal-title":"JAMA oncology"},{"key":"2043_CR24","doi-asserted-by":"publisher","unstructured":"Kraskov A, St\u00f6gbauer H, Grassberger P. Estimating mutual information. Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Jun.\u00a069(6 Pt 2):066138. https:\/\/doi.org\/10.1103\/PhysRevE.69.066138. Erratum in: Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jan.\u00a083(1 Pt 1):019903.","DOI":"10.1103\/PhysRevE.69.066138"},{"key":"2043_CR25","unstructured":"Pedregosa, F., et al., Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 2011. 12: p. 2825\u20132830."},{"key":"2043_CR26","first-page":"122","volume":"120","author":"Bradski G The","year":"2000","unstructured":"Bradski G. The OpenCV Library. Dr. Dobb\u2019s Journal of Software Tools. 2000;120:122-125.","journal-title":"Dr. Dobb\u2019s Journal of Software Tools."},{"key":"2043_CR27","doi-asserted-by":"crossref","unstructured":"Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., & Thomas, N.E., A method for normalizing histology slides for quantitative analysis. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro,\u00a02009. 1107-1110.","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"2043_CR28","volume-title":"International Conference on Medical image computing and computer-assisted intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in International Conference on Medical image computing and computer-assisted intervention. 2015. Springer."},{"key":"2043_CR29","doi-asserted-by":"crossref","unstructured":"Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., & Catanzaro, B., High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2017. 8798-8807.","DOI":"10.1109\/CVPR.2018.00917"},{"key":"2043_CR30","unstructured":"Liu, G., Vahdat, A., Huang, D., Theodorou, E.A., Nie, W., & Anandkumar, A., I2SB: Image-to-Image Schr\u00f6dinger Bridge. International Conference on Machine Learning,\u00a02023."},{"key":"2043_CR31","doi-asserted-by":"publisher","first-page":"103747","DOI":"10.1016\/j.media.2025.103747","volume":"106","author":"F Arslan","year":"2025","unstructured":"Arslan, F., et al., Self-consistent recursive diffusion bridge for medical image translation. Medical Image Analysis, 2025. 106: p. 103747.","journal-title":"Medical Image Analysis"},{"key":"2043_CR32","doi-asserted-by":"crossref","unstructured":"Pachitariu, M., M. Rariden, and C. Stringer, Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv, 2025: p. 2025.04. 28.651001.","DOI":"10.1101\/2025.04.28.651001"},{"issue":"1","key":"2043_CR33","doi-asserted-by":"publisher","first-page":"26638","DOI":"10.1038\/s41598-025-10885-3","volume":"15","author":"K Shigehara","year":"2025","unstructured":"Shigehara, K., et al., The size of CD8+ infiltrating T cells is a prognostic marker for esophageal squamous cell carcinoma. Scientific Reports, 2025. 15(1): p. 26638.","journal-title":"Scientific Reports"},{"key":"2043_CR34","doi-asserted-by":"crossref","unstructured":"van der Walt S, S.J., Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T, scikit-image: image processing in Python. PeerJ, 2014. 2: p. e453.","DOI":"10.7717\/peerj.453"},{"key":"2043_CR35","unstructured":"Jeffrey A. Clark and Pillow Contributors. (2025). Pillow (Version 11.1.0) [Computer software]. https:\/\/pillow.readthedocs.io\/"},{"key":"2043_CR36","doi-asserted-by":"crossref","unstructured":"Eldahshan, K.A., Youssef, M.I., Masameer, E.H., & Mustafa, M.A., Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis based on HSV Color Space. International Journal of Computer Applications,\u00a02014. 90:\u00a048-51.","DOI":"10.5120\/15590-4426"},{"issue":"3","key":"2043_CR37","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/TBME.2011.2179298","volume":"59","author":"X Qi","year":"2011","unstructured":"Qi, X., et al., Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Transactions on Biomedical Engineering, 2011. 59(3): p. 754-765.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"2043_CR38","doi-asserted-by":"publisher","first-page":"141","DOI":"10.11613\/BM.2015.015","volume":"25","author":"D Giavarina","year":"2015","unstructured":"Giavarina, D., Understanding bland altman analysis. Biochemia medica, 2015. 25(2): p. 141-151.","journal-title":"Biochemia medica"},{"key":"2043_CR39","unstructured":"Virtanen, P., et al., SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods,\u00a02020. 17(3), 261-272. doi.org"},{"issue":"31","key":"2043_CR40","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.21105\/joss.01026","volume":"3","author":"R Vallat","year":"2018","unstructured":"Vallat, R., Pingouin: statistics in Python. Journal of Open Source Software, 2018. 3(31): p. 1026.","journal-title":"Journal of Open Source Software"},{"issue":"2","key":"2043_CR41","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo, T.K. and M.Y. Li, A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 2016. 15(2): p. 155-163.","journal-title":"Journal of Chiropractic Medicine"},{"issue":"7","key":"2043_CR42","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1093\/oncolo\/oyae054","volume":"29","author":"D Fortman","year":"2024","unstructured":"Fortman, D., et al., Automated quantitative CD8+ tumor-infiltrating lymphocytes and tumor mutation burden as independent biomarkers in melanoma patients receiving front-line anti-PD-1 immunotherapy. The Oncologist, 2024. 29(7): p. 619-628.","journal-title":"The Oncologist"},{"key":"2043_CR43","doi-asserted-by":"publisher","first-page":"100437","DOI":"10.1016\/j.jpi.2025.100437","volume":"17","author":"N Shvetsov","year":"2025","unstructured":"Shvetsov, N., et al., Fast TILs\u2014A pipeline for efficient TILs estimation in non-small cell Lung cancer. Journal of Pathology Informatics, 2025. 17: p. 100437.","journal-title":"Journal of Pathology Informatics"},{"issue":"1","key":"2043_CR44","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1159\/000346834","volume":"8","author":"M Reinisch","year":"2013","unstructured":"Reinisch, M., et al., Side effects of standard adjuvant and neoadjuvant chemotherapy regimens according to age groups in primary breast cancer. Breast Care, 2013. 8(1): p. 60-66.","journal-title":"Breast Care"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02043-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02043-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02043-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:10:49Z","timestamp":1784232649000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02043-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":44,"alternative-id":["2043"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02043-6","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"28 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All data were de-identified prior to analysis, and the requirement for informed consent was waived by the Institutional Review Board (IRB) of Asan Medical Center due to the retrospective nature of the study.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. The requirement for informed consent was waived by the IRB.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}