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Pre-trained models and utilities for deep learning on medical images in Python (Keras/TensorFlow)
| Date | Stars |
|---|---|
| 2026-07-31 | 258 |
| 2026-08-03 | 259 |
| 2026-08-06 | 259 |
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[](https://pypi.org/project/antspynet/)
[](code_of_conduct.md)
[](https://hub.docker.com/repository/docker/antsx/antspynet)
[](https://pubmed.ncbi.nlm.nih.gov/33907199/)
# Advanced Normalization Tools for Deep Learning in Python (ANTsPyNet)
A collection of deep learning architectures and applications ported to the Python language and tools for basic medical image processing. Based on `keras` and `tensorflow` with cross-compatibility with our R analog [ANTsRNet](https://github.com/ANTsX/ANTsRNet/). ANTsPyNet provides three high-level features:
- A large collection of common deep learning architectures for medical imaging that can be initialized
- Various pre-trained deep learning models to perform key medical imaging tasks
- Utility functions to improve training and evaluating of deep learning models on medical images
<p align="middle">
<img src="docs/figures/coreANTsXNetTools.png" width="600" />
</p>
## Overview
<details>
<summary>Installation</summary>
### Binaries
The easiest way to install ANTsPyNet is via pip.
```
python -m pip install antspynet
```
### From Source
Alternatively, you can download and install from source.
```
git clone https://github.com/ANTsX/ANTsPyNet
cd ANTsPyNet
python -m pip install .
```
</details>
<!--
## Quickstart
The core functionality that ANTsPyNet provides is the ability to initialize a Deep Learning model based on our large collection of model architectures specifically tailored for medical images. You can then train these initialized models using your standard `keras` or `tensorflow` workflows.
An example of initializing a deep learning model based on the is provided here:
```python
from antspynet.architectures import create_autoencoder_model
model = create_autoencoder_model((784, 500, 500, 2000, 10))
model.summary()
```
We also provide a collection of pre-trained models that can perform key medical imaging processing tasks such as brain extraction, segmentation, cortical thickness, and more. An example of reading a brain image using `ANTsPy` and then performing brain extraction using our pre-trained model in `ANTsPyNet` is presented here:
```python
import ants
import antspynet
t1 = ants.image_read(antspynet.get_antsxnet_data('mprage_hippmapp3r'))
seg = antspynet.brain_extraction(t1, modality="t1", verbose=True)
ants.plot(t1, overlay=seg, overlay_alpha=0.5)
```
-->
<details>
<summary>Architectures</summary>
### Image voxelwise segmentation/regression
- [U-Net (2-D, 3-D)](https://arxiv.org/abs/1505.04597)
- [U-Net + ResNet (2-D, 3-D)](https://arxiv.org/abs/1608.04117)
- [Dense U-Net (2-D, 3-D)](https://arxiv.org/pdf/1709.07330.pdf)
### Image classification/regression
- [AlexNet (2-D, 3-D)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
- [VGG (2-D, 3-D)](https://arxiv.org/abs/1409.1556)
- [ResNet (2-D, 3-D)](https://arxiv.org/abs/1512.03385)
- [ResNeXt (2-D, 3-D)](https://arxiv.org/abs/1611.05431)
- [WideResNet (2-D, 3-D)](http://arxiv.org/abs/1605.07146)
- [DenseNet (2-D, 3-D)](https://arxiv.org/abs/1608.06993)
### Object detection
### Image super-resolution
- [Super-resolution convolutional neural network (SRCNN) (2-D, 3-D)](https://arxiv.org/abs/1501.00092)
- [Expanded super-resolution (ESRCNN) (2-D, 3-D)](https://arxiv.org/abs/1501.00092)
- [Denoising auto encoder super-resolution (DSRCNN) (2-D, 3-D)]()
- [Deep denoise super-resolution (DDSRCNN) (2-D, 3-D)](https://arxiv.org/abs/1606.08921)
- [ResNet super-resolution (SRResNet) (2-D, 3-D)](https://arxiv.org/abs/1609.04802)
- [Deep back-projection network (DBPN) (2-D, 3-D)](https://arxiv.org/abs/1803.02735)
- [Super resoluExcerpt of 15,017 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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