Task-driven framework using large models for digital pathology
Microscopy is an indispensable tool for collecting biomedical information in pathological diagnosis, but manual annotation, measurement and interpretation are labor-intensive and costly. Here, we propose a task-driven framework powered by large models that excel in visual analysis and real-time control, paving the way for the next generation of microscopes. We achieve proof-of-concept success on clinical tasks, specifically in adaptive analysis of H&E-stained liver tissue slides. This work demonstrates the advanced capabilities for future digital pathology, setting a new standard for intelligent, efficient, and real-time analysis in clinical applications.
Authors
- Yingke Xu (ORCID: https://orcid.org/0000-0002-8317-0608)
- Tianyu Ma (ORCID: https://orcid.org/0000-0003-3260-7125)
- Jiahui Yu (ORCID: https://orcid.org/0009-0000-4565-4217)
- Feng Chen (ORCID: https://orcid.org/0000-0002-4827-1294)
- Jing Zhang (ORCID: https://orcid.org/0000-0001-6517-8289)
Institutions
- Westlake University (CN)
- Children's Hospital of Zhejiang University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Communications Biology
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1038/s42003-024-07303-1
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China
- Hangzhou Science and Technology Bureau
- Natural Science Foundation of Zhejiang Province