Initial commit -- Adding calibration loss specific to segmentation

[Bala93]

Description

Model calibration has helped in developing reliable deep learning models. In this pull request, I have added a new loss function NACL (https://arxiv.org/abs/2303.06268, https://arxiv.org/abs/2401.14487) which has shown promising results for both discriminative and calibration in segmentation.

Future Plans: Currently, MONAI has some of the alternative loss functions (Label Smoothing, and Focal Loss), but it doesn't have the calibration specific loss functions (https://arxiv.org/abs/2111.15430, https://arxiv.org/abs/2209.09641). Besides, these methods are better evaluated with calibration metrics, Expected Calibration Error (https://lightning.ai/docs/torchmetrics/stable/classification/calibration_error.html).

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [ ] Breaking change (fix or new feature that would cause existing functionality to change).
• [x] New tests added to cover the changes.
• [x] Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• [x] Quick tests passed locally by running ./runtests.sh --quick --unittests --disttests.
• [x] In-line docstrings updated.
• [x] Documentation updated, tested make html command in the docs/ folder.