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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A framework for high-performance medical image processing, neural network inference and visualization
| Date | Stars |
|---|---|
| 2026-07-31 | 514 |
| 2026-08-01 | 514 |
| 2026-08-02 | 514 |
| 2026-08-06 | 514 |
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5.0
growth rate 0.00%/day
 [](https://github.com/FAST-Imaging/FAST/discussions) [](https://fast-imaging.github.io/download-stats.html) [](https://fast-imaging.github.io/download-stats.html) [](https://fast-imaging.github.io) FAST is an open-source framework developed by researchers at the Norwegian University of Science and Technology (NTNU) and SINTEF. The main goal of FAST is to make it easier to do high-performance processing, neural network inference, and visualization of medical images utilizing multi-core CPUs and GPUs. To achieve this, FAST use modern C++, OpenCL and OpenGL, and neural network inference libraries such as TensorRT, OpenVINO, TensorFlow and ONNX Runtime. ### Get started See installation instructions for [Windows](https://fast-imaging.github.io/install-windows.html), [Ubuntu Linux](https://fast-imaging.github.io/install-ubuntu-linux.html), [macOS](https://fast-imaging.github.io/install-mac.html) and [Docker containers](https://fast-imaging.github.io/fast-containers.html). To start using the framework, check out the [C++ tutorials](https://fast-imaging.github.io/cpp-tutorials.html) or the [Python tutorials](https://fast-imaging.github.io/python-tutorials.html). Learn best by example? Check out all the examples for [C++](https://fast-imaging.github.io/cpp-examples.html) and [Python](https://fast-imaging.github.io/python-examples.html). For more examples and documentation, go to [fast-imaging.github.io](https://fast-imaging.github.io). Need help? Post your questions on the [Discussions](https://github.com/FAST-Imaging/FAST/discussions/new?category=q-a) page. ### Main features * **Data streaming** – Processing pipelines in FAST can handle both static and dynamic/temporal data without any change to the code. FAST can stream data from movie files, your webcamera, an Intel RealSense camera, a sequence of images and even directly from ultrasound scanners such as Clarius. * **Deep learning** – FAST provides a common interface for neural networks supporting different model formats (ONNX, protobuf, SavedModel, OpenVINO, UFF) and backends (Google TensorFlow, NVIDIA TensorRT, Intel OpenVINO, Microsoft ONNX Runtime), making it possible to create real-time neural network pipelines. * **High-level data management** – Data objects in FAST represent data, such as an image, on all processors. FAST keeps data coherent across the different storage areas thereby removing the burden of explicit memory handling from the developer. * **Wide data format support** - FAST supports several data formats (DICOM, metaimage (MHD), NIFTI, regular jpg/png/bmp images, videos, HDF5, VTK polydata, whole slide images, ultrasound file format) and data types (images 2D and 3D, grayscale and color, image pyramids, surface mesh, vertices, lines, text ++). * **High performance algorithms** – FAST has several high performance parallel OpenCL implementations of common algorithms, such as marching cubes surface extraction, Gaussian smoothing, non-local means, block matching tracking and seeded region growing. * **Fast concurrent visualization** – Rendering and computation are done in separate threads to ensure smooth responsive visualizations. Several types of visualizations are supported both 3D (mesh, point, line, image slice and volume rendering) and 2D (2D image, image slice and segmentation/label rendering, whole slide image (WSI) pyramids). * **Interoperability** – FAST can be used with Py
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André Pedersen · @DIPSAS · Norway
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The Gitter Badger · Gitter
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:26ce39086a157caf, topic:deep-learning