期刊
NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-22518-0
关键词
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资金
- UK Medical Research Council [MR/K015826/1, FC001999]
- Wellcome Trust [FC001999, 203276/Z/16/Z]
- Gulbenkian Foundation
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [101001332]
- European Molecular Biology Organization (EMBO) Installation Grant [EMBO-2020-IG-4734]
- MRC Skills development fellowship [MR/T027924/1]
- Academy of Finland
- Sigrid Juselius Foundation
- University of Turku foundation
- Turku Doctoral Program in Molecular Medicine (TuDMM)
- Abo Akademi University Research Foundation
- Drug Discovery and Diagnostics strategic fund
- Victoriastiftelsen
- German Research Foundation (DFG) [JU3110/1-1]
- German Federal Ministry of Research and Education [01IS18026C]
- German Science Foundation [SFB1177]
- European Molecular Biology Organization [8589]
- CNRS [AO2016]
- Wellcome Trust
- Royal Society Sir Henry Dale Fellowship [206670/Z/17/Z]
- Chan Zuckerberg Biohub
- Francis Crick Institute
- Cancer Research UK [FC001999]
- European Research Council (ERC) [101001332] Funding Source: European Research Council (ERC)
- Wellcome Trust [206670/Z/17/Z] Funding Source: Wellcome Trust
ZeroCostDL4Mic is an entry-level platform that simplifies access to deep learning by leveraging the free, cloud-based computational resources of Google Colab. Researchers can train and apply key deep learning networks for various microscopy tasks without coding expertise. The platform provides quantitative tools for model evaluation and optimization.
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic.
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