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Copyright (c) 2018, Justus Schock, Christoph Haarburger All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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![logo](docs/_static/logo/delira.svg “delira - Deep Learning in Radiology”)

# Delira - Deep Learning in Radiology Authors: [Justus Schock, Christoph Haarburger, Oliver Rippel](AUTHORS.rst)

## Introduction Delira was developed as a deep learning framework for medical images such as CT or MRI. Currently, it works on arbitrary data (based on [NumPy](http://www.numpy.org/)).

Based on [PyTorch](https://pytorch.org), [batchgenerators](https://github.com/MIC-DKFZ/batchgenerators) and [trixi](https://github.com/MIC-DKFZ/trixi) it provides a framework for * Dataset loading * Dataset sampling * Augmentation (multi-threaded) including 3D images with any number of channels * A generic trainer class that implements the training process * Already implemented [models](delira/models) used in medical image processing and exemplaric implementations of most used models in general (like Resnet) * Web-based monitoring using [Visdom](https://github.com/facebookresearch/visdom) * Model save and load functions

Delira supports classification and regression problems as well as generative adversarial networks.

## Installation

### Choose Backend

Currently the only available backend is [PyTorch](https://pytorch.org) (or no backend at all) but we are working on support for [TensorFlow](https://tensorflow.org) as well. 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.

For instructions to install delira with a specific backend, please have a look at [the corresponding docs](https://delira.readthedocs.io/en/latest/getting_started.html#installation)

### Installation without a backend (from source) To install delira without a backend (not all functionalities may be work due to a missing backend) you can simply run: * pip install git+https://github.com/justusschock/delira.git

## Getting Started The best way to learn how to use is to have a look at the [tutorial notebook](notebooks/tutorial_delira.ipynb). Example implementations for classification problems, segmentation approaches and GANs are also provided in the [notebooks](notebooks) folder.

## Contributing If you find a bug or have an idea for an improvement, please have a look at our [contribution guideline](CONTRIBUTING.md).

Platform: UNKNOWN Requires-Python: >3.5

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