2025/01/02 by Bruno M. Saraiva, Inês Cunha, António D. Brito +6 · 2 voices · 9 citations
Biochemistry, Genetics and Molecular Biology · #Advanced Electron Microscopy Techniques and Applications #Advanced Fluorescence Microscopy Techniques #Artificial intelligence #Cell Image Analysis Techniques #Computational science #Computer graphics (images) #Computer science #Database #Graphics #Graphics processing unit #Image (mathematics) #Image processing #Operating system #Parallel computing #Python (programming language) #Workflow
paper · pdf · doi:10.1038/s41592-024-02562-6
published in Nature Methods 22(2), 283-286 (Nature Portfolio)
openalex publication_date 2025/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.