Eisen: a python package for solid deep learning
This package addresses the need for easier deep learning workflows in medical imaging and related fields, but it is incremental as it builds on existing PyTorch frameworks.
The authors introduced Eisen, a Python package built on PyTorch to simplify deep learning implementation, particularly for medical image analysis and computer vision, by providing tools for data handling, training loops, and experiment management.
Eisen is an open source python package making the implementation of deep learning methods easy. It is specifically tailored to medical image analysis and computer vision tasks, but its flexibility allows extension to any application. Eisen is based on PyTorch and it follows the same architecture of other packages belonging to the PyTorch ecosystem. This simplifies its use and allows it to be compatible with modules provided by other packages. Eisen implements multiple dataset loading methods, I/O for various data formats, data manipulation and transformation, full implementation of training, validation and test loops, implementation of losses and network architectures, automatic export of training artifacts, summaries and logs, visual experiment building, command line interface and more. Furthermore, it is open to user contributions by the community. Documentation, examples and code can be downloaded from http://eisen.ai.