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- W2809239478 abstract "Abstract As biological imaging datasets increase in size, deep neural networks are considered vital tools for efficient image segmentation. While a number of different network architectures have been developed for segmenting even the most challenging biological images, community access is still limited by the difficulty of setting up complex computational environments and processing pipelines, and the availability of compute resources. Here, we address these bottlenecks, providing a ready-to-use image segmentation solution for any lab, with a pre-configured, publicly available, cloud-based deep convolutional neural network on Amazon Web Services (AWS). We provide simple instructions for training and applying CDeep3M for segmentation of large and complex 2D and 3D microscopy datasets of diverse biomedical imaging modalities." @default.
- W2809239478 created "2018-06-29" @default.
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- W2809239478 date "2018-06-21" @default.
- W2809239478 modified "2023-10-15" @default.
- W2809239478 title "CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy" @default.
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- W2809239478 doi "https://doi.org/10.1101/353425" @default.
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