* SageMaker training - part 1 (https://github.com/DIAGNijmegen/picai_baseline/pull/18)
* Training scripts for SSL nnU-Net on SageMaker
- Improve the robustness of train.py to make debugging easier
- Install dependencies of nnU-Net in SageMaker's requirements.txt
- Install modified nnU-Net without requirements, to prevent requiring internet access
* Convert U-Net `plan_overview` to a function
* Training scripts for SSL U-Net on SageMaker
* Add cross-validation splits with 10 cases for debugging
* Add missing U-Net dependency
* Sagemaker training part2 (https://github.com/DIAGNijmegen/picai_baseline/pull/26)
* Data preprocessing: Improved specification of preprocessing settings,
and logging of unexpected parameters
* Improved training in distributed environments (such as SageMaker)
* nnU-Net: improved preprocessing performance with `nnUNet_tl`
* nnU-Net: preprocess scans to a maximum physical size of 81 x 192 x 192
mm
* U-Net: improve robustness of preprocessing, by skipping cases with
label interpolation error
* U-Net: add missing dependency
* Cross-validation splits: add PI-CAI PubPrivTrain, unit-tests, and
bugfix when using multiple at once
* Performance optimization
* PI-CAI PubPrivTrain cross-validation splits (https://github.com/DIAGNijmegen/picai_baseline/pull/21)
* Improved logging preprocessing script
* Improve input specification preprocessing scripts
* Location of the dataset and working direction can now be specified in three ways:
- Using command line argument (i.e., `--workdir=...`)
- Using environment variable (i.e. `ENV workdir=...`, for example in Dockerfile/docker run command/Python)
- Mount folders to the default location (i.e., with `-v` flag in `docker run`)
* Images and labels can now be located at separate locations.
* Update documentation
* Fix for nnU-Net inference softmax (https://github.com/DIAGNijmegen/picai_baseline/pull/12)
- Fix how nnUNet softmax predictions are converted from their .npz format of the cropped image to the physical extent of the original image
- Evaluate while cropping away exterior predictions
* Single configurable nnU-Net evaluation script
* Bugfixes
* Fix UNet path handling (https://github.com/DIAGNijmegen/picai_baseline/pull/19)
* Update to correct FL gamma (https://github.com/DIAGNijmegen/picai_baseline/commit/149cd4d9012cf5e4ef9ac2a47894def1f179ad75)
* Cleanup
* Encapsulate plan_overview in a function
* Command line options for preprocessing settings (https://github.com/DIAGNijmegen/picai_baseline/pull/16)