r20240307

About

This release updates the training codebase and adds a newer variant of the contrast-agnostic model (details below).

What's Changed

• Update readme with citation info and arxiv badge by @naga-karthik in #88
• Fixed documentation on inference by @jcohenadad in #91
• Fixed documentation on inference (follow-up) by @jcohenadad in #93
• Make inference CLI callable from anywhere by @jcohenadad in #95
• add info about dataset split used for training contrast-agnostic model by @naga-karthik in #97
• Simplify inference script by @naga-karthik in #100
• Port towards config file-based training by @naga-karthik in #90

Other notable changes

• The model in this release is trained with binarized soft labels (hence the name soft_bin) as opposed to directly training on soft labels as in the model in release v2.0
• In addition to the monai-based nnunet model, this release also adds the feature to train other models as well (e.g. SwinUNETR, MedNeXT, etc.)
• Three new classes of CSA evaluation scripts are added -- (1) evaluating CSA across different models, (2) evaluating CSA across different resolutions, and (3) evaluating CSA across different resolutions.
  • A unified script analyze_csa_across.py is added for generating CSA violin plots across different classes mentioned above.

Full Changelog: v2.0...v2.1