{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:21:04Z","timestamp":1791638464369,"version":"4.3.4"},"reference-count":45,"publisher":"Elsevier BV","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.elsevier.com\/tdm\/userlicense\/1.0\/"},{"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.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008349","name":"Universit\u00e4t Duisburg-Essen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.cmpb.2025.109206","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T16:06:45Z","timestamp":1767974805000},"page":"109206","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":29,"special_numbering":"C","title":["CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models"],"prefix":"10.1016","volume":"277","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-7644","authenticated-orcid":false,"given":"Fabian","family":"H\u00f6rst","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7137-2624","authenticated-orcid":false,"given":"Moritz","family":"Rempe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9464-2382","authenticated-orcid":false,"given":"Helmut","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3405-0556","authenticated-orcid":false,"given":"Lukas","family":"Heine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Keyl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-0682","authenticated-orcid":false,"given":"Jens","family":"Kleesiek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.cmpb.2025.109206_b1","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1038\/s44222-023-00096-8","article-title":"Artificial intelligence for digital and computational pathology","volume":"1","author":"Song","year":"2023","journal-title":"Nat. Rev. Bioeng."},{"key":"10.1016\/j.cmpb.2025.109206_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2021.103492","article-title":"A computational tumor-infiltrating lymphocyte assessment method comparable with visual reporting guidelines for triple-negative breast cancer","volume":"70","author":"Sun","year":"2021","journal-title":"EBioMedicine"},{"issue":"1","key":"10.1016\/j.cmpb.2025.109206_b3","doi-asserted-by":"crossref","DOI":"10.1038\/s41523-020-0154-2","article-title":"Report on computational assessment of tumor infiltrating lymphocytes from the international immuno-oncology biomarker working group","volume":"6","author":"Amgad","year":"2020","journal-title":"Npj Breast Cancer"},{"key":"10.1016\/j.cmpb.2025.109206_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103143","article-title":"CellViT: Vision transformers for precise cell segmentation and classification","volume":"94","author":"H\u00f6rst","year":"2024","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.cmpb.2025.109206_b5","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","article-title":"Segmentation of nuclei in histopathology images by deep regression of the distance map","volume":"38","author":"Naylor","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2025.109206_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101563","article-title":"Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images","volume":"58","author":"Graham","year":"2019","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.cmpb.2025.109206_b7","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: a generalist algorithm for cellular segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.cmpb.2025.109206_b8","series-title":"2022 IEEE International Symposium on Biomedical Imaging Challenges","first-page":"1","article-title":"Nuclei instance segmentation and classification in histopathology images with stardist","author":"Weigert","year":"2022"},{"key":"10.1016\/j.cmpb.2025.109206_b9","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16144","article-title":"Scaling vision transformers to gigapixel images via hierarchical self-supervised learning","author":"Chen","year":"2022"},{"issue":"3","key":"10.1016\/j.cmpb.2025.109206_b10","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.cmpb.2025.109206_b11","doi-asserted-by":"crossref","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","author":"Vorontsov","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.cmpb.2025.109206_b12","series-title":"Virchow2: Scaling self-supervised mixed magnification models in pathology","author":"Zimmermann","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b13","series-title":"RudolfV: A foundation model by pathologists for pathologists","author":"Dippel","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b14","first-page":"1","article-title":"A whole-slide foundation model for digital pathology from real-world data","author":"Xu","year":"2024","journal-title":"Nat."},{"key":"10.1016\/j.cmpb.2025.109206_b15","series-title":"Pannuke dataset extension, insights and baselines","author":"Gamper","year":"2020"},{"issue":"1","key":"10.1016\/j.cmpb.2025.109206_b16","doi-asserted-by":"crossref","DOI":"10.1038\/s41523-024-00663-1","article-title":"A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes","volume":"10","author":"Liu","year":"2024","journal-title":"Npj Breast Cancer"},{"key":"10.1016\/j.cmpb.2025.109206_b17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9532","article-title":"Every annotation counts: Multi-label deep supervision for medical image segmentation","author":"Reiss","year":"2021"},{"issue":"2","key":"10.1016\/j.cmpb.2025.109206_b18","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2020","journal-title":"Nat. Methods"},{"key":"10.1016\/j.cmpb.2025.109206_b19","doi-asserted-by":"crossref","DOI":"10.1038\/s41551-024-01223-5","article-title":"A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies","author":"Huang","year":"2024","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2025.109206_b20","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"23902","article-title":"OCELOT: overlapped cell on tissue dataset for histopathology","author":"Ryu","year":"2023"},{"key":"10.1016\/j.cmpb.2025.109206_b21","series-title":"MIDOG++: A comprehensive multi-domain dataset for mitotic figure detection","author":"Aubreville","year":"2023"},{"key":"10.1016\/j.cmpb.2025.109206_b22","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giac037","article-title":"NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer","volume":"11","author":"Amgad","year":"2022","journal-title":"GigaScience"},{"issue":"2","key":"10.1016\/j.cmpb.2025.109206_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2023.100688","article-title":"Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists","volume":"4","author":"Komura","year":"2023","journal-title":"Patterns"},{"key":"10.1016\/j.cmpb.2025.109206_b24","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"684","article-title":"Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification","author":"Graham","year":"2021"},{"key":"10.1016\/j.cmpb.2025.109206_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Automated machine learning: Review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.cmpb.2025.109206_b26","doi-asserted-by":"crossref","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.Y. Lo, et al., Segment anything, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.cmpb.2025.109206_b27","series-title":"Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology","first-page":"150","article-title":"Enhancing cell detection in histopathology images: A ViT-based U-net approach","author":"Li","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b28","series-title":"Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology","first-page":"123","article-title":"Detecting cells in histopathology images with a ResNet ensemble model","author":"Lafarge","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b29","series-title":"Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology","first-page":"130","article-title":"Enhancing cell detection via FC-HarDNet and tissue segmentation: OCELOT 2023 challenge approach","author":"Lo","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b30","series-title":"Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology","first-page":"138","article-title":"Dense prediction of cell centroids using tissue context and cell refinement","author":"Millward","year":"2024"},{"key":"10.1016\/j.cmpb.2025.109206_b31","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","article-title":"Gland segmentation in colon histology images: The glas challenge contest","volume":"35","author":"Sirinukunwattana","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2025.109206_b32","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.media.2018.12.001","article-title":"MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images","volume":"52","author":"Graham","year":"2019","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.cmpb.2025.109206_b33","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1056\/NEJMp1607591","article-title":"Toward a shared vision for cancer genomic data","volume":"375","author":"Grossman","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.cmpb.2025.109206_b34","series-title":"World Cancer Report","author":"World Health Organization: Regional Office for Europe","year":"2020"},{"key":"10.1016\/j.cmpb.2025.109206_b35","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9404","article-title":"Panoptic segmentation","author":"Kirillov","year":"2019"},{"key":"10.1016\/j.cmpb.2025.109206_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102685","article-title":"One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification","volume":"83","author":"Graham","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2025.109206_b37","series-title":"Carbon emissions and large neural network training","author":"Patterson","year":"2021"},{"key":"10.1016\/j.cmpb.2025.109206_b38","series-title":"Quantifying the carbon emissions of machine learning","author":"Lacoste","year":"2019"},{"key":"10.1016\/j.cmpb.2025.109206_b39","series-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.cmpb.2025.109206_b40","series-title":"Bildverarbeitung F\u00dcr Die Medizin 2024","first-page":"356","article-title":"Accelerating artificial intelligence-based whole slide image analysis with an optimized preprocessing pipeline","author":"H\u00f6rst","year":"2024"},{"issue":"1","key":"10.1016\/j.cmpb.2025.109206_b41","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cmpb.2025.109206_b42","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TETCI.2023.3281864","article-title":"PointNu-Net: Keypoint-assisted convolutional neural network for simultaneous multi-tissue histology nuclei segmentation and classification","volume":"8","author":"Yao","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.cmpb.2025.109206_b43","series-title":"Pattern Recognition and Computer Vision","first-page":"122","article-title":"Probability-based nuclei detection and critical-region guided instance segmentation","author":"Zhong","year":"2023"},{"key":"10.1016\/j.cmpb.2025.109206_b44","doi-asserted-by":"crossref","first-page":"giaf011","DOI":"10.1093\/gigascience\/giaf011","article-title":"A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks","volume":"14","author":"Schuiveling","year":"2025","journal-title":"GigaScience"},{"key":"10.1016\/j.cmpb.2025.109206_b45","unstructured":"T.Y. Ross, G. Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2980\u20132988."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260725006212?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260725006212?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:34:52Z","timestamp":1773981292000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260725006212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":45,"alternative-id":["S0169260725006212"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2025.109206","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2025.109206","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"109206"}}