Adapting General-Purpose Foundation Models for X-ray Ptychography in Low-Data Regimes

A new benchmark named PtychoBench has been introduced to improve the automation of workflows in advanced microscopy, with a particular focus on ptychographic analysis. This initiative seeks to adapt general-purpose foundation models, including language and vision-language models, for specialized scientific applications. The development addresses significant challenges related to domain adaptation, especially in scenarios where data availability is limited. By leveraging these foundation models, PtychoBench aims to facilitate more efficient and accurate analysis in X-ray ptychography, a technique critical for high-resolution imaging. The benchmark provides a structured framework to evaluate and enhance model performance in low-data regimes, which is a common constraint in scientific imaging tasks. This approach reflects ongoing efforts to bridge the gap between general AI models and domain-specific scientific needs, promoting greater automation and precision in microscopy workflows.