Bridging scales: a perspective on macro-to-micro transfer for microscopy foundation models

Foundation models (FMs) are transforming artificial intelligence for science, but microscopy FMs remain constrained by limited, heterogeneous, and information-sparse data. We propose a Macro-to-Micro paradigm in which large-scale macroscopic pretraining establishes a broad visual representation, followed by microscopy-specific adaptation that introduces biological specificity and modality-dependent imaging knowledge. This staged strategy enables scarce microscopy data to specialize a mature representation rather than build one from scratch. Combined with domain-aware adaptation and a coordinated microscopy data ecosystem, Macro-to-Micro provides a data-efficient roadmap toward foundation models that ground general visual intelligence in biological knowledge and the physics of image formation.