ArticleNature cancer2026
A universal visual foundation model for computational cytopathology.
Xueyi Zheng, Ke Zheng, Jue Wang, Xinke Zhang, Kai Li, Zihan Zhao, Chaofeng Li, Haohua Chen, Ruixuan Wang, Junpeng Lai and 4 more
Abstract read
In one paragraphArticle in Nature cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
14 authors.
Xueyi Zheng *Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID http://orcid.org/0000-0003-1386-7052 Ke Zheng *Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jue Wang *Department of Cellular and Molecular Diagnostics Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Xinke Zhang *Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID http://orcid.org/0000-0002-3527-2140 Kai LiDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zihan ZhaoDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Chaofeng LiArtificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Haohua ChenArtificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Junpeng LaiDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Dan XieDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. xiedan@sysucc.org.cn.ORCID http://orcid.org/0000-0003-2242-3138 Muyan CaiDepartment of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. caimy@sysucc.org.cn.ORCID http://orcid.org/0000-0002-4646-4391 Funding
China Postdoctoral Science Foundation 2023TQ0392China Postdoctoral Science Foundation 2024M753786National Natural Science Foundation of China (National Science Foundation of China) 82172646National Natural Science Foundation of China (National Science Foundation of China) 82403737National Natural Science Foundation of China (National Science Foundation of China) 82503854
6 · The paper itselfAbstract
Cytopathology is central to cancer screening and diagnosis but remains labor-intensive, motivating the development of scalable computational solutions. Here, we present CROWN (Cytology visual foundation netwoRk Optimized With self-supervised learNing), a universal visual foundation model for computational cytopathology. CROWN was pretrained on more than 10 million cytology image patches using a DINOv2-based self-supervised framework, without requiring manual annotations during pretraining. We evaluated CROWN in 202 task-setting combinations spanning classification, segmentation, detection, retrieval and slide-level prediction across private institutional cohorts and public datasets. CROWN achieved the best overall performance among established pretrained encoders across diverse benchmark settings, with accuracy exceeding 95% in 48 patch-level classification evaluations and 98% in 22 of them. These findings support CROWN as a scalable visual backbone for general-purpose computational cytopathology across heterogeneous datasets and task settings.
What OpenQuestion holds
Textmetadata
Read underepoch 390