Abstract
Automated and accurate profiling of microscopy images from small-scale to high-throughput is becoming an essential procedure in basic and applied biological research. Here, we present Microsnoop, a novel deep learning-based representation tool trained on large-scale microscopy images using masked self-supervised learning, which eliminates the need for manual annotation. Microsnoop is able to unbiasedly profile a wide range of complex and heterogeneous images, including single-cell, fully-imaged and batch-experiment data. We evaluated the performance of Microsnoop using seven high-quality datasets, containing over 358,000 images and 1,270,000 single cells with varying resolutions and channels from cellular organelles to tissues. Our results demonstrate Microsnoop’s robustness and state-of-the-art performance in all biological applications, outperforming previous generalist and even custom algorithms. Furthermore, we presented its potential contribution for multi-modal studies. Microsnoop is highly inclusive of GPU and CPU capabilities, and can be freely and easily deployed on local or cloud computing platforms.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
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