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Delira - Deep Learning in Radiology

Authors: Justus Schock, Oliver Rippel, Christoph Haarburger

Introduction

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).

Based on batchgenerators and 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 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.

Installation

Choose Backend

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
None pip install delira pip install git+https://github.com/justusschock/delira.git Training not possible if backend is not installed separately
torch pip install delira[torch] git clone https://github.com/justusschock/delira.git && cd delira && pip install .[torch] delira with torch backend supports mixed-precision training via NVIDIA/apex (must be installed separately).
tensorflow pip install delira[tensorflow] git clone https://github.com/justusschock/delira.git && cd delira && pip install .[tensorflow] the tensorflow backend is still very experimental and lacks some features
Full pip install delira[full] git clone https://github.com/justusschock/delira.git && cd delira && pip install .[full] All backends will be installed.

Docker

The easiest way to use delira is via docker (with the nvidia-runtime for GPU-support) and using the Dockerfile or the prebuild-images.

Chat

We have a community chat on slack. If you need an invitation, just follow this link.

Getting Started

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.

Documentation

The docs are hosted on ReadTheDocs/Delira. The documentation of the latest master branch can always be found at the project's github page.

Contributing

If you find a bug or have an idea for an improvement, please have a look at our contribution guideline.

Project details


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