All notable changes to NiftyNet are documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
0.2.2 - 2018-01-30
- Improvements for running validation iterations during training
- Bugs when running validation iterations during training
- Minor bugs in loss function modules, histogram standardisation, user parameter parsing
0.2.1 - 2017-12-14
- Support for custom network / application as external modules
- Unified workspace directory via global configuration functionalities
- Model zoo for network / data sharing
- Automatic training / validation / test sets splitting
- Validation iterations during training
- Regression application
- 2D / 3D resampler layer
- Versioning functionality for better issue tracking
- Academic paper release: "NiftyNet: a deep-learning platform for medical imaging"
- How-to guides and a new theme for the API and examples documentation
0.2.0 - 2017-09-08
- Support for unsupervised learning networks, including GANs and auto-encoders
- An application engine for managing low-level operations required by different types of high-level applications
- NiftyNet is now available on the Python Package Index:
pip install niftynet - NiftyNet website up and running: http://niftynet.io
- API reference published online: http://niftynet.rtfd.io/en/dev/py-modindex.html
- NiftyNet source code mirrored on GitHub: https://github.com/NifTK/NiftyNet
- 5 new network implementations:
- DenseVNet
- HolisticNet
- SimpleGAN
- SimulatorGAN
- VariationalAutoencoder (VAE)
- Bugs (30+ issues resolved)
- Source code open sourced (CMICLab, GitHub)
- Initial PyPI package release
- Refactored sub-packages including
engine,application,layer,network - Command line entry points
- NiftyNet logo
- Bugs in data augmentation, I/O, sampler