{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T11:10:14Z","timestamp":1778325014131,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00521-025-11723-3","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:36:00Z","timestamp":1775831760000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identity-preserving latent diffusion for enhanced protein localization: a novel lymphocyte-inspired approach"],"prefix":"10.1007","volume":"38","author":[{"given":"Hanaa Salem","family":"Marie","sequence":"first","affiliation":[]},{"given":"Moatasem M.","family":"Draz","sequence":"additional","affiliation":[]},{"given":"Waleed Abd","family":"Elkhalik","sequence":"additional","affiliation":[]},{"given":"Mostafa","family":"Elbaz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"issue":"5","key":"11723_CR1","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1002\/path.6155","volume":"260","author":"J Thagaard","year":"2023","unstructured":"Thagaard J et al (2023) Pitfalls in machine learning-based assessment of tumor\u2010infiltrating lymphocytes in breast cancer: a report of the international Immuno\u2010Oncology biomarker working group on breast cancer. J Pathol 260(5):498\u2013513","journal-title":"J Pathol"},{"key":"11723_CR2","doi-asserted-by":"publisher","first-page":"C2","DOI":"10.1016\/j.vaccine.2010.07.022","volume":"28","author":"M Moser","year":"2010","unstructured":"Moser M, Leo O (2010) Key concepts in immunology. Vaccine 28:C2\u2013C13","journal-title":"Vaccine"},{"issue":"45","key":"11723_CR3","doi-asserted-by":"publisher","first-page":"2302530","DOI":"10.1002\/adma.202302530","volume":"35","author":"X Zheng","year":"2023","unstructured":"Zheng X, Zhang X, Chen T-T, Watanabe I (2023) Deep learning in mechanical metamaterials: from prediction and generation to inverse design. Adv Mater 35(45):2302530","journal-title":"Adv Mater"},{"key":"11723_CR4","unstructured":"Chale MW (2022) Generative methods, meta-learning, and meta-heuristics for robust cyber defense"},{"key":"11723_CR5","doi-asserted-by":"crossref","unstructured":"Chen Y et al (2024) Sckansformer: fine-grained classification of bone marrow cells via kansformer backbone and hierarchical attention mechanisms, IEEE Journal of Biomedical and Health Informatics","DOI":"10.1109\/JBHI.2024.3471928"},{"key":"11723_CR6","doi-asserted-by":"publisher","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni A et al (2023) Diffusion models in medical imaging: a comprehensive survey. Med Image Anal 88:102846","journal-title":"Med Image Anal"},{"key":"11723_CR7","unstructured":"Oluwafemi EPO (2024) Advancing colonoscopy analysis through text-to-image synthesis using generative AI for intelligent data augmentation, image classification, and segmentation, Morgan State University"},{"key":"11723_CR8","unstructured":"Fan K (2025) Machine learning techniques for medical image analysis with data scarcity. University of Southampton"},{"key":"11723_CR9","unstructured":"V\u00e1zquez-Garc\u00eda C, Mart\u00ednez-Murcia FJ, Rom\u00e1n FS, G\u00f3rriz JM (2024) A review of latent representation models in neuroimaging, arXiv preprint arXiv:2412.19844"},{"key":"11723_CR10","unstructured":"Manduchi L et al (2024) On the challenges and opportunities in generative AI, arXiv preprint arXiv:2403.00025"},{"key":"11723_CR11","doi-asserted-by":"crossref","unstructured":"Hagos DH, Battle R, Rawat DB (2024) Recent advances in generative Ai and large language models: current status, challenges, and perspectives. IEEE Trans Artif Intell","DOI":"10.1109\/TAI.2024.3444742"},{"key":"11723_CR12","doi-asserted-by":"crossref","unstructured":"Rives A et al (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, In: Proceedings of the National Academy of Sciences, 118(15), pp. e2016239118","DOI":"10.1073\/pnas.2016239118"},{"issue":"7","key":"11723_CR13","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","volume":"173","author":"DM Camacho","year":"2018","unstructured":"Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ (2018) Next-generation machine learning for biological networks. Cell 173(7):1581\u20131592","journal-title":"Cell"},{"issue":"8","key":"11723_CR14","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1038\/s41556-022-00960-6","volume":"24","author":"B Andrews","year":"2022","unstructured":"Andrews B et al (2022) Imaging cell biology. Nat Cell Biol 24(8):1180\u20131185","journal-title":"Nat Cell Biol"},{"key":"11723_CR15","unstructured":"Sapkota R, Raza S, Shoman M, Paudel A, Karkee M (2025) Image, text, and speech data augmentation using multimodal llms for deep learning: a survey, arXiv preprint arXiv:2501.18648"},{"issue":"3","key":"11723_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446374","volume":"54","author":"D Saxena","year":"2021","unstructured":"Saxena D, Cao J (2021) Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Comput Surv (CSUR) 54(3):1\u201342","journal-title":"ACM Comput Surv (CSUR)"},{"key":"11723_CR17","doi-asserted-by":"crossref","unstructured":"Kushwaha V, Nandi GC Study of prevention of mode collapse in generative adversarial network (gan), In: (2020) IEEE 4th Conference on Information & Communication Technology (CICT), IEEE, 2020, pp. 1\u20136","DOI":"10.1109\/CICT51604.2020.9312049"},{"key":"11723_CR18","doi-asserted-by":"crossref","unstructured":"Chen H (2021) Challenges and corresponding solutions of generative adversarial networks (GANs): a survey study, In: Journal of Physics: Conference Series, IOP Publishing, p. 012066","DOI":"10.1088\/1742-6596\/1827\/1\/012066"},{"issue":"7","key":"11723_CR19","doi-asserted-by":"publisher","first-page":"071704","DOI":"10.1115\/1.4053859","volume":"144","author":"L Regenwetter","year":"2022","unstructured":"Regenwetter L, Nobari AH, Ahmed F (2022) Deep generative models in engineering design: a review. J Mech Des 144(7):071704","journal-title":"J Mech Des"},{"key":"11723_CR20","doi-asserted-by":"crossref","unstructured":"Zhang H et al (2024) MosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI. BioRxiv","DOI":"10.1101\/2024.08.01.606222"},{"issue":"1","key":"11723_CR21","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s13059-024-03226-6","volume":"25","author":"C Skok Gibbs","year":"2024","unstructured":"Skok Gibbs C, Mahmood O, Bonneau R, Cho K (2024) PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization. Genome Biol 25(1):88","journal-title":"Genome Biol"},{"issue":"9","key":"11723_CR22","doi-asserted-by":"publisher","first-page":"e9198","DOI":"10.15252\/msb.20199198","volume":"16","author":"R Lopez","year":"2020","unstructured":"Lopez R, Gayoso A, Yosef N (2020) Enhancing scientific discoveries in molecular biology with deep generative models. Mol Syst Biol 16(9):e9198","journal-title":"Mol Syst Biol"},{"key":"11723_CR23","doi-asserted-by":"crossref","unstructured":"Shahbazian R, Greco S (2023) Generative adversarial networks assist missing data imputation: a comprehensive survey & evaluation, IEEE Access","DOI":"10.1109\/ACCESS.2023.3306721"},{"issue":"1","key":"11723_CR24","doi-asserted-by":"publisher","first-page":"23936","DOI":"10.1038\/s41598-024-73976-7","volume":"14","author":"GM Mahmoud","year":"2024","unstructured":"Mahmoud GM, Elbaz M, Alqahtani F, Alginahi Y, Said W (2024) A novel 8-connected pixel identity GAN with neutrosophic (ECP-IGANN) for missing imputation. Sci Rep 14(1):23936","journal-title":"Sci Rep"},{"key":"11723_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42:1\u201313","journal-title":"J Med Syst"},{"issue":"1","key":"11723_CR26","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.gpb.2022.10.001","volume":"21","author":"S Fang","year":"2023","unstructured":"Fang S et al (2023) Computational approaches and challenges in Spatial transcriptomics. Genom Proteom Bioinform 21(1):24\u201347","journal-title":"Genom Proteom Bioinform"},{"issue":"1","key":"11723_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11280-024-01315-x","volume":"28","author":"K Chen","year":"2025","unstructured":"Chen K, Li Y, Zhu X, Zhang W, Hu B (2025) A vision-language model with multi-granular knowledge fusion in medical imaging. World Wide Web 28(1):1\u201321","journal-title":"World Wide Web"},{"key":"11723_CR28","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/RBME.2010.2081975","volume":"3","author":"AP Dhawan","year":"2010","unstructured":"Dhawan AP, D\u2019Alessandro B, Fu X (2010) Optical imaging modalities for biomedical applications. IEEE Rev Biomed Eng 3:69\u201392","journal-title":"IEEE Rev Biomed Eng"},{"issue":"28","key":"11723_CR29","doi-asserted-by":"publisher","first-page":"1900737","DOI":"10.1002\/smll.201900737","volume":"15","author":"A Arandian","year":"2019","unstructured":"Arandian A et al (2019) Optical imaging approaches to monitor static and dynamic cell-on\u2010chip platforms: a tutorial review. Small 15(28):1900737","journal-title":"Small"},{"issue":"1","key":"11723_CR30","doi-asserted-by":"publisher","first-page":"8543","DOI":"10.1038\/s41598-024-58766-5","volume":"14","author":"K Kairi\u0161s","year":"2024","unstructured":"Kairi\u0161s K et al (2024) Visualisation of gene expression within the context of tissues using an X-ray computed tomography-based multimodal approach. Sci Rep 14(1):8543","journal-title":"Sci Rep"},{"key":"11723_CR31","doi-asserted-by":"publisher","first-page":"103486","DOI":"10.1016\/j.micron.2023.103486","volume":"172","author":"P Sowmiya","year":"2023","unstructured":"Sowmiya P et al (2023) Optically active organic and inorganic nanomaterials for biological imaging applications: a review. Micron 172:103486","journal-title":"Micron"},{"issue":"1","key":"11723_CR32","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/JSTSP.2015.2505402","volume":"10","author":"C Kervrann","year":"2015","unstructured":"Kervrann C, Sorzano C\u00d3S, Acton ST, Olivo-Marin J-C, Unser M (2015) A guided tour of selected image processing and analysis methods for fluorescence and electron microscopy. IEEE J Selec Topics Signal Process 10(1):6\u201330","journal-title":"IEEE J Selec Topics Signal Process"},{"issue":"2","key":"11723_CR33","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","volume":"9","author":"Z Ren","year":"2022","unstructured":"Ren Z, Fang F, Yan N, Wu Y (2022) State of the Art in defect detection based on machine vision. Int J Precision Eng Manufacturing-Green Technol 9(2):661\u2013691","journal-title":"Int J Precision Eng Manufacturing-Green Technol"},{"key":"11723_CR34","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1109\/TCI.2020.3006727","volume":"6","author":"CD Bahadir","year":"2020","unstructured":"Bahadir CD, Wang AQ, Dalca AV, Sabuncu MR (2020) Deep-learning-based optimization of the under-sampling pattern in MRI. IEEE Trans Comput Imaging 6:1139\u20131152","journal-title":"IEEE Trans Comput Imaging"},{"key":"11723_CR35","doi-asserted-by":"crossref","unstructured":"Zhang H, Hu Z, Luo C, Zuo W, Wang M (2018) Semantic image inpainting with progressive generative networks, In: Proceedings of the 26th ACM international conference on Multimedia, pp. 1939\u20131947","DOI":"10.1145\/3240508.3240625"},{"key":"11723_CR36","doi-asserted-by":"publisher","first-page":"104187","DOI":"10.1016\/j.dsp.2023.104187","volume":"141","author":"A Wali","year":"2023","unstructured":"Wali A, Naseer A, Tamoor M, Gilani SAM (2023) Recent progress in digital image restoration techniques: a review. Digit Signal Proc 141:104187","journal-title":"Digit Signal Proc"},{"key":"11723_CR37","unstructured":"Donkor SA, Walsh ME, Titus AJ (2024) Computing in the life sciences: from early algorithms to modern AI, arXiv preprint arXiv:2406.12108"},{"key":"11723_CR38","unstructured":"Zheng D, Huang B (2024) CELL-Diff: unified diffusion modeling for protein sequences and microscopy images. BioRxiv, p. 2024.10. 15.618585"},{"key":"11723_CR39","unstructured":"Fu C et al (2024) A latent diffusion model for protein structure generation, In: Learning on Graphs Conference, PMLR, pp. 29: 1\u201329: 17"},{"issue":"12","key":"11723_CR40","doi-asserted-by":"publisher","first-page":"i32","DOI":"10.1093\/bioinformatics\/bts230","volume":"28","author":"J Li","year":"2012","unstructured":"Li J, Xiong L, Schneider J, Murphy RF (2012) Protein subcellular location pattern classification in cellular images using latent discriminative models. Bioinformatics 28(12):i32\u2013i39","journal-title":"Bioinformatics"},{"key":"11723_CR41","doi-asserted-by":"publisher","first-page":"1451261","DOI":"10.3389\/fimmu.2024.1451261","volume":"15","author":"X Li","year":"2024","unstructured":"Li X et al (2024) Computational staining of CD3\/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model. Front Immunol 15:1451261","journal-title":"Front Immunol"},{"key":"11723_CR42","doi-asserted-by":"crossref","unstructured":"Madeira M, Thanou D, Frossard P (2023) Tertiary lymphoid structures generation through graph-based diffusion, In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 37\u201353","DOI":"10.1007\/978-3-031-55088-1_4"},{"key":"11723_CR43","doi-asserted-by":"crossref","unstructured":"Liu Y et al (2024) Pan-cancer single-cell and subcellular characterization of lymphoid aggregates and tertiary lymphoid structures using high-plex spatial molecular imaging, Cancer Research, vol. 84(6) Supplement, pp. 1152\u20131152","DOI":"10.1158\/1538-7445.AM2024-1152"},{"key":"11723_CR44","doi-asserted-by":"crossref","unstructured":"Song W et al (2023) Medical Image Generation based on latent diffusion models, In: International Conference on Artificial Intelligence Innovation (ICAII), IEEE, 2023, pp. 89\u201393","DOI":"10.1109\/ICAII59460.2023.10497435"},{"issue":"6","key":"11723_CR45","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.1093\/bioinformatics\/btz844","volume":"36","author":"Y-Y Xu","year":"2020","unstructured":"Xu Y-Y, Shen H-B, Murphy RF (2020) Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images. Bioinformatics 36(6):1908\u20131914","journal-title":"Bioinformatics"},{"key":"11723_CR46","doi-asserted-by":"crossref","unstructured":"Yellapragada S, Graikos A, Prasanna P, Kurc T, Saltz J, Samaras D (2024) Pathldm: text conditioned latent diffusion model for histopathology, In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5182\u20135191","DOI":"10.1109\/WACV57701.2024.00510"},{"key":"11723_CR47","doi-asserted-by":"publisher","first-page":"2789","DOI":"10.1182\/blood-2019-130831","volume":"134","author":"M Hav","year":"2019","unstructured":"Hav M et al (2019) Highly multiplexed single cell Spatial analysis of the tumor microenvironment in lymphoma predicts clinical outcomes. Blood 134:2789","journal-title":"Blood"},{"issue":"1","key":"11723_CR48","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.cell.2024.10.045","volume":"188","author":"Y-T Deng","year":"2025","unstructured":"Deng Y-T et al (2025) Atlas of the plasma proteome in health and disease in 53,026 adults. Cell 188(1):253\u2013271","journal-title":"Cell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11723-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11723-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11723-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T10:58:46Z","timestamp":1778324326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11723-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":48,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["11723"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11723-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"6 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"252"}}