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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.
Neural networks toolbox focused on medical image analysis
| Date | Stars |
|---|---|
| 2026-07-24 | 376 |
| 2026-07-25 | 376 |
| 2026-07-28 | 376 |
| 2026-07-30 | 376 |
| 2026-07-31 | 376 |
| 2026-08-06 | 376 |
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# Neurite A neural networks toolbox with a focus on medical image analysis in pytorch. > ⚠️ **Warning**: neurite is under active development. We are in the process of finalizing the structure for PyTorch -- interfaces may change. ## Install To use the Neurite library, either clone this repository and install the requirements listed in `setup.py` or install directly with pip. ``` pip install neurite ``` ### Pre-commit hooks This repo uses `pre-commit` to run `pycodestyle` before commits. Install once after cloning: ```bash pip install pre-commit pre-commit install ``` You can also run the check manually: ```bash pre-commit run pycodestyle --all-files ``` **For users who want to use the stable TensorFlow version**, use either `pip install neurite`, or pull/clone from the `dev-tensorflow` branch. ## Main tools - [nn.functional](neurite/nn/functional.py): tensor ops for smoothing, resampling, interpolation, masking, and spatial math. - [nn.modules](neurite/nn/modules.py): reusable stateful layers, losses, and preprocessing blocks. - [nn.models](neurite/nn/models.py): prebuilt architectures for arbitrary spatial dimensions (1d, 2d, 3d). - [py.plot](neurite/py/plot.py): plotting tools for tensor slices, volumes, and flow fields. - [utils.utils](neurite/utils/utils.py): lightweight factory and helper functions and tools. ## Papers: If you use this code, please cite: **Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation** [Adrian V. Dalca](http://adalca.mit.edu), [John Guttag](https://people.csail.mit.edu/guttag/), [Mert R. Sabuncu](http://sabuncu.engineering.cornell.edu/) CVPR 2018. [ [PDF](http://www.mit.edu/~adalca/files/papers/cvpr2018_priors.pdf) | [arxiv](http://arxiv.org/abs/1903.03148) | [bibtex](citations.bib) ] If you are using any of the sparse/imputation functions, please cite: **Unsupervised Data Imputation via Variational Inference of Deep Subspaces** [Adrian V. Dalca](http://adalca.mit.edu), [John Guttag](https://people.csail.mit.edu/guttag/), [Mert R. Sabuncu](http://sabuncu.engineering.cornell.edu/) Arxiv preprint 2019 [ [arxiv](https://arxiv.org/abs/1903.03503) | [bibtex](citations.bib) ] ## Development: We welcome contributions; please make sure your code respects `pep8`, except for `E731,W291,W503,W504`, by running: ```pycodestyle --ignore E731,W291,W503,W504 --max-line-length 100 /path/to/neurite``` Please open an [issue](https://github.com/adalca/neurite/issues) [preferred] or contact Adrian Dalca at [email protected] for question related to `neurite`. ## Use/demos: Parts of `neurite` were used in [VoxelMorph](http://voxelmorph.mit.edu) and [brainstorm](https://github.com/xamyzhao/brainstorm/), which we encourage you to check out!
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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