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Computational pathology in precision oncology: Evolution from task-specific models to foundation models

文献类型：期刊论文

作者Wang, Yuhao6,7; Gu, Yunjie6,7; Zhang, Xueyuan2; Wang, Baizhi6,7; Wang, Rundong6,7; Li, Xiaolong6,7; Liu, Yudong3; Qu, Fengmei4; Ren, Fei3; Yan, Rui6,7
刊名CHINESE MEDICAL JOURNAL
出版日期2025-11-20
卷号138期号:22页码:2868-2878
关键词Computational pathology Artificial intelligence Deep learning Foundation models Precision oncology
ISSN号0366-6999
DOI10.1097/CM9.0000000000003790
英文摘要With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
资助项目Science and Technology Innovation Key R&D Program of Chongqing[CSTB2022TIAD-STX0008] ; Natural Science Foundation of China[62402473] ; Natural Science Foundation of China[62271465] ; Suzhou Basic Research Program[SYG202338]
WOS研究方向General & Internal Medicine
语种英语
WOS记录号WOS:001619101600006
出版者LIPPINCOTT WILLIAMS & WILKINS
源URL[http://119.78.100.204/handle/2XEOYT63/43073]  
专题中国科学院计算技术研究所
通讯作者Ren, Fei; Yan, Rui; Zhou, S. Kevin
作者单位1.Univ Sci & Technol China, State Key Lab Precis & Intelligent Chem, Hefei 230026, Anhui, Peoples R China
2.Chongqing Zhijian Life Technol Co LTD, Chongqing 400039, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
4.Jinfeng Lab, Chongqing 401329, Peoples R China
5.Jiangsu Prov Key Lab Multimodal Digital Twin Techn, Suzhou 215123, Jiangsu, Peoples R China
6.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China
7.USTC, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou 215123, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yuhao,Gu, Yunjie,Zhang, Xueyuan,et al. Computational pathology in precision oncology: Evolution from task-specific models to foundation models[J]. CHINESE MEDICAL JOURNAL,2025,138(22):2868-2878.
APA Wang, Yuhao.,Gu, Yunjie.,Zhang, Xueyuan.,Wang, Baizhi.,Wang, Rundong.,...&Zhou, S. Kevin.(2025).Computational pathology in precision oncology: Evolution from task-specific models to foundation models.CHINESE MEDICAL JOURNAL,138(22),2868-2878.
MLA Wang, Yuhao,et al."Computational pathology in precision oncology: Evolution from task-specific models to foundation models".CHINESE MEDICAL JOURNAL 138.22(2025):2868-2878.

入库方式： OAI收割

来源：计算技术研究所

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