Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.02.25.530004v1?rss=1
Authors: Xun, D., Wang, R., Wang, Y.
Abstract:
Accurate and automated representation 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.
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