From cells to patients:Multiscale computational pathology in the era of foundation models and vision-language systemsOA
Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide images for patient-level prognostic profiling.While traditional deep learning approaches have achieved remarkable success in specific tasks,the recent emergence of large-scale foundation models and vision-language models has precipitated a paradigm shift in the field.These data-driven systems,characterized by their robust representation learning and semantic reasoning capabilities,are redefining how we analyze pathological data across diverse spatial scales.In this review,we provide a comprehensive synthesis of this transformation through a multiscale lens.We systematically survey the application of foundation models and vision-language models in deciphering biological complexity,ranging from cell-level segmentation and tissue phenotyping to whole-slide image-level prediction and multimodal integration.Furthermore,we critically analyze the limitations of current approaches,such as interpretability,computational efficiency,and data bias,then outline promising future directions for developing holistic,context-aware systems that bridge the gap between pixel-level features and patient-centric clinical decision-making.
Zeyu Gao;Jiusong Ge;Junbo Lu;Lei Wu;Hannah Clayton;Di Zhang;Jiashuai Liu;Inês Prata Machado;Yan Chen;Mireia Crispín‑Ortuzar;Chen Li
Department of Oncology,University of Cambridge,Cambridge CB20AH,United KingdomSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,ChinaSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,ChinaSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,ChinaDepartment of Oncology,University of Cambridge,Cambridge CB20AH,United KingdomSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,ChinaSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,ChinaDepartment of Oncology,University of Cambridge,Cambridge CB20AH,United KingdomDepartment of Oncology,Xijing Hospital of Air Force Medical University,Xi’an Shaanxi 710032,ChinaDepartment of Oncology,University of Cambridge,Cambridge CB20AH,United KingdomSchool of Computer Science and Technology,Xi’an Jiaotong University,Xi’an Shaanxi 710049,China
医药卫生
Computational pathologyFoundation modelMultiscale modeling
《Intelligent Oncology》 2026 (3)
P.66-75,10
supported by the National Science and Technology Major Project(Grant No.:2025ZD0544802)the Key Research and Development Program of Shaanxi Province(Grant No.:2024SFGJHX-32)the Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.:2023BEG02023)the Noncommunicable Chronic Diseases-National Science and Technology Major Project(Grant No.:2024ZD0527700)the project“Research on Key Technologies for Full-Chain Intelligent Pathological Diagnosis”of The First Affiliated Hospital of Xi''an Jiaotong University(Grant No.:HX202440)。
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