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Task-driven framework using large models for digital pathology
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DOI：10.1038/s42003-024-07303-1.png)
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Abstract
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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.
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