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Delira - Deep Learning in Radiology
Delira was originally developed as a deep learning framework for medical images such as CT or MRI. Currently, it works on arbitrary data (based on NumPy).
- Dataset loading
- Dataset sampling
- Augmentation (multi-threaded) including 3D images with any number of channels
- A generic trainer class that implements the training process for all backends
- Already implemented models used in medical image processing and exemplaric implementations of most used models in general (like Resnet)
- Web-based monitoring using Visdom
- Tensorboard monitoring
- Model save and load functions
Delira supports classification and regression problems as well as generative adversarial networks and segmentation tasks.
Currently the only available backends are PyTorch and TensorFlow(or no backend at all). If you want to add another backend, please open an issue (it should not be hard at all) and we will guide you during the process of doing so.
|Backend||Binary Installation||Source Installation||Notes|
||Training not possible if backend is not installed separately|
||All backends will be installed.|
The best way to learn how to use is to have a look at the tutorial notebook. Example implementations for classification problems, segmentation approaches and GANs are also provided in the notebooks folder.
If you find a bug or have an idea for an improvement, please have a look at our contribution guideline.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size delira-0.4.1.tar.gz (109.8 kB)||File type Source||Python version None||Upload date||Hashes View hashes|