Rationale & Philosophy

There are nowadays a lot of repositories with code for parsing medical imaging data in PyTorch, however, some challenges are still present:

• many of these repositories doesn’t contain a single word of documentation;
• many are just for classification datasets;
• the majority of them aren’t maintained;
• most of them takes your freedom due to design mistakes;
• many models in these repositories are locked in monolithic code where you cannot repurpose for your own goals;
• many of them doesn’t contain a single line of testing;
• missing examples on how to use them.

The idea of this framework is to provide a elegant design to solve the issues of medical imaging in PyTorch. The design principles of this framework are the following:

• easy reusable components;
• code well-documented;
• documentation with examples and manuals;
• extensive testing coverage;
• easy to integrate in your pipeline;
• support for a variety of medical imaging sources;
• close as possible to the PyTorch design.

With that in mind, there is a long road ahead and contributions are always welcome.