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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.
AI Toolkit for Healthcare Imaging
| Date | Stars |
|---|---|
| 2026-07-24 | 8456 |
| 2026-07-25 | 8469 |
| 2026-07-28 | 8469 |
| 2026-07-30 | 8469 |
| 2026-08-06 | 8469 |
Today
— stars today
This week
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Momentum
15.0
growth rate 0.00%/day
<p align="center"> <img src="https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/MONAI-logo-color.png" width="50%" alt='project-monai'> </p> **M**edical **O**pen **N**etwork for **AI**  [](https://opensource.org/licenses/Apache-2.0) [](https://arxiv.org/abs/2211.02701) [](https://badge.fury.io/py/monai) [](https://hub.docker.com/r/projectmonai/monai) [](https://anaconda.org/conda-forge/monai) [](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [](https://monai.readthedocs.io/en/latest/) [](https://codecov.io/gh/Project-MONAI/MONAI) [](https://piptrends.com/package/monai) MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of the [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Its ambitions are as follows: - Developing a community of academic, industrial and clinical researchers collaborating on a common foundation; - Creating state-of-the-art, end-to-end training workflows for healthcare imaging; - Providing researchers with the optimized and standardized way to create and evaluate deep learning models. ## Features > _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._ - flexible pre-processing for multi-dimensional medical imaging data; - compositional & portable APIs for ease of integration in existing workflows; - domain-specific implementations for networks, losses, evaluation metrics and more; - customizable design for varying user expertise; - multi-GPU multi-node data parallelism support. ## Requirements MONAI works with the [currently supported versions of Python](https://devguide.python.org/versions), and depends directly on NumPy and PyTorch with many optional dependencies. * Major releases of MONAI will have dependency versions stated for them. The current state of the `dev` branch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so. * PyTorch support covers [the current version](https://github.com/pytorch/pytorch/releases) plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release. * Our support policy for other dependencies adheres for the most part to [SPEC0](https://scientific-python.org/specs/spec-0000), where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported. *
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:42f4a49e63cbaa99, topic:deep-learning, topic:pytorch