Add 3D segmentation calibration tutorial

Signed-off-by: Theo Barfoot <theo.barfoot@gmail.com>

Author: theo-barfoot

README.md

@@ -101,6 +101,13 @@ Each user is responsible for checking the content of datasets and the applicable
 You can read details about adding a tutorial in our [CONTRIBUTING GUIDELINES](CONTRIBUTING.md).
 
 ### 4. List of notebooks and examples
+#### <ins>**Calibration**</ins>
+##### [Training and evaluating calibrated segmentation models](./calibration/segmentation_calibration.ipynb)
+This tutorial trains two models on complete 3D MRI volumes from the Medical Segmentation Decathlon
+`Task04_Hippocampus` dataset to demonstrate calibration metrics, reliability diagrams, the Ignite calibration
+handler, and L1-ACE auxiliary training. It compares a validation-selected hard L1-ACE configuration with a controlled
+segmentation baseline on separate held-out test volumes.
+
 #### <ins>**2D classification**</ins>
 ##### [mednist_tutorial](./2d_classification/mednist_tutorial.ipynb)
 This notebook shows how to easily integrate MONAI features into existing PyTorch programs.

calibration/README.md

@@ -0,0 +1,25 @@
+# Segmentation model calibration
+
+This folder contains a self-contained tutorial for evaluating and improving the marginal class-wise calibration of
+semantic segmentation models. It trains on complete 3D volumes from the Medical Segmentation Decathlon
+`Task04_Hippocampus` MRI dataset. The notebook downloads the approximately 28 MB archive automatically and reuses
+the directory configured by `MONAI_DATA_DIRECTORY`.
+
+The notebook demonstrates MONAI's calibration metrics, low-level bin statistics, Ignite handler, and hard- and
+soft-binned L1 Average Calibration Error losses. It uses complete-volume train/validation/test cohorts and compares
+a segmentation baseline with one hard L1-ACE configuration chosen in validation-only preliminary experiments for
+its calibration improvement with minimal Dice reduction. The focused comparison discusses the associated
+publication's 1:1:1 objective, finite-bin estimates, and the limitations of auxiliary calibration training.
+
+For a one-epoch CI smoke test, run:
+
+```bash
+export MONAI_DATA_DIRECTORY=/path/to/persistent/monai-data
+./runner.sh -t calibration/segmentation_calibration.ipynb
+```
+
+`runner.sh` rewrites `max_epochs` and `val_interval` to one. To reproduce the saved full experiment, open the notebook
+in Jupyter and run all cells without that rewrite. A CUDA GPU is strongly recommended for the two 3D training runs.
+
+The notebook requires a MONAI build containing `HardL1ACELoss` and `SoftL1ACELoss`. Until those APIs are available in
+an official MONAI package, run it in an environment with the corresponding MONAI core contribution installed editable.