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https://github.com/ncullen93/torchsample
Train AI models efficiently on medical images using any framework
https://github.com/ncullen93/torchsample
deep-learning keras medical-imaging neuroimaging pytorch
Last synced: 21 days ago
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Train AI models efficiently on medical images using any framework
- Host: GitHub
- URL: https://github.com/ncullen93/torchsample
- Owner: nitrain
- License: agpl-3.0
- Created: 2017-03-01T02:42:12.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2024-06-13T19:43:58.000Z (3 months ago)
- Last Synced: 2024-07-10T14:43:22.293Z (2 months ago)
- Topics: deep-learning, keras, medical-imaging, neuroimaging, pytorch
- Language: Python
- Homepage:
- Size: 3.68 MB
- Stars: 1,858
- Watchers: 49
- Forks: 302
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - torch-sampling
- Awesome-pytorch-list - torch-sampling - memory or out-of-memory data. (Pytorch & related libraries / Other libraries:)
README
# Nitrain: a medical imaging-native AI framework
[](https://coveralls.io/github/nitrain/nitrain?branch=main)
[](https://github.com/nitrain/nitrain/actions/workflows/test.yml)
Nitrain (formerly torchsample) is a framework-agnostic python library for sampling and augmenting medical images, training models on medical imaging datasets, and visualizing results in a medical imaging context.
The nitrain library is unique in that it makes training models as simple as possible by providing reasonable defaults and a high-level of abstraction. It also supports multiple frameworks - torch, tensorflow, and keras - with a goal to add even more.
Full examples of training medical imaging AI models using nitrain can be found at the [Tutorials](https://github.com/nitrain/tutorials) page. If you are interested more generally in medical imaging AI, check out [Practical medical imaging AI techniques with Python](https://book.nitrain.dev) (expected early 2025).
## Quickstart
Here is an example of using nitrain to a semantic segmentation model that demonstrates much of the core functionality.
```python
import nitrain as nt
from nitrain.readers import ImageReader, ColumnReader
# create dataset from folder of images + participants file
dataset = nt.Dataset(inputs=ImageReader('sub-*/anat/*_T1w.nii.gz'),
outputs=ImageReader('sub-*/anat/*_aparc+aseg.nii.gz'),
transforms={
'inputs': tx.NormalizeIntensity(0,1),
('inputs', 'outputs'): tx.Resize((64,64,64))
},
base_dir='~/desktop/ds004711/')
# create loader with random transforms
loader = nt.Loader(dataset,
images_per_batch=4,
sampler=nt.SliceSampler(batch_size = 32, axis = 2)
transforms={
'inputs': tx.RandomNoise(sd=0.2)
})
# create model from architecture
arch_fn = nt.fetch_architecture('unet', dim=2)
model = arch_fn(input_image_size=(64,64,1),
mode='segmentation')
# create trainer and fit model
trainer = nt.Trainer(model, task='segmentation')
trainer.fit(loader, epochs=100)
```
## Installation
The latest release of nitrain can be installed from pypi:
```
pip install nitrain
```
Or you can install the latest development version directly from github:
```
python -m pip install git+github.com/nitrain/nitrain.git
```
### Dependencies
The [ants](https://www.github.com/antsx/antspy) python package is a key dependency that allows you to efficiently read, operate on, and visualize medical images. Additionally, you can use keras (tf.keras or keras3), tensorflow, or pytorch as backend for creating your models.
## Resources
The following links can be helpful in becoming more familiar with nitrain.
- Introduction tutorials [[Link](https://github.com/nitrain/tutorials/tree/main/introduction)]
- Segmentation examples [[Link](https://github.com/nitrain/tutorials/tree/main/segmentation)]
- Classification examples [[Link](https://github.com/nitrain/tutorials/tree/main/classification)]
- Registration examples [[Link](https://github.com/nitrain/tutorials/tree/main/registration)]
You can also visit the [ANTsPy](https://github.com/antsx/antspy) repo to learn more about handling medical images and performing traditional medical imaging analysis.
## Contributing
If you have a question or bug report the best way to get help is by posting an issue on the GitHub page. I would be happy to welcome any new contributors or ideas to the project. If you want to add code, the best way to get started is by posting an issue or contacting me at [email protected].
You can support this work by starring the repository or posting a feature request in the issues tab. These actions help increase the project's impact and community reach.