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bioRxiv · 10.1101/2023.08.13.553080

NanoPyx: super-fast bioimage analysis powered by adaptive machine learning

Saraiva, B. M.·Cunha, I. M.·Brito, A. D.·Follain, G.·Portela, R.·Haase, R.·Pereira, P. M.·Jacquemet, G.·Henriques, R.
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Abstract

To overcome the challenges posed by large and complex microscopy datasets, we have developed NanoPyx, an adaptive bioimage analysis framework designed for high-speed processing. At the core of NanoPyx is the Liquid Engine, an agent-based machine-learning system that predicts acceleration strategies for image analysis tasks. Unlike traditional single-algorithm methods, the Liquid Engine generates multiple CPU and GPU code variations using a meta-programming system, creating a competitive environment where different algorithms are benchmarked against each other to achieve optimal performance under the user"s computational environment. In initial experiments focusing on super-resolution analysis methods, the Liquid Engine demonstrated an over 10-fold computational speed improvement by accurately predicting the ideal scenarios to switch between algorithmic implementations. NanoPyx is accessible to users through a Python library, code-free Jupyter notebooks, and a napari plugin, making it suitable for individuals regardless of their coding proficiency. Furthermore, the optimisation principles embodied by the Liquid Engine have broader implications, extending their applicability to various high-performance computing fields.

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Saraiva, B. M., Cunha, I. M., Brito, A. D., Follain, G., Portela, R., Haase, R., Pereira, P. M., Jacquemet, G., Henriques, R.. 2023-08-14. NanoPyx: super-fast bioimage analysis powered by adaptive machine learning. https://doi.org/10.1101/2023.08.13.553080

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