Showing 4 open source projects for "mri software"

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    Resque

    Resque

    Ruby library for creating background jobs and processing them

    Resque is a Redis-backed Ruby library for creating background jobs, placing them on multiple queues, and processing them later. Resque (pronounced like "rescue") is a Redis-backed library for creating background jobs, placing those jobs on multiple queues, and processing them later. Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do...
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  • 2
    fastMRI

    fastMRI

    A large open dataset + tools to speed up MRI scans using ML

    fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data...
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  • 3
    fog

    fog

    The Ruby cloud services library

    Whether you need compute, dns, storage, or a multitude of other services, fog provides an accessible entry point and facilitates cross-service compatibility. Just getting started working with cloud resources? You are not alone, and having so many complicated options makes it hard to know where to start. fog delivers the knowledge of cloud experts to you, helping you to bootstrap your cloud usage and guiding you as your own expertise develops. By coding with fog from the start you avoid...
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  • 4
    ivantk

    ivantk

    Image-based Vascular Analysis Toolkit

    The Image-based Vascular Analysis Toolkit is a set of multiplatform C++ libraries for vascular analysis of (3D) medical images, typically CT or MRI. It can be considered as an extension of the Insight Toolkit (ITK) for vascular image analysis, with methods for detection, extraction and modeling of vascular structures.
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