Add 3D segmentation calibration tutorial

[theo-barfoot]

Description

Adds an introductory tutorial for the average calibration losses proposed in Project-MONAI/MONAI#9092.

The notebook explains predictive calibration, why it matters for medical image segmentation, and how to interpret reliability diagrams. It then compares a segmentation-only baseline with one validation-selected Hard L1-ACE configuration on complete 3D volumes from Medical Segmentation Decathlon Task04 Hippocampus.

The example intentionally keeps the experiment simple: two training runs with identical initialization, data order, augmentations, and optimization settings. The calibrated objective uses a 1:1:0.30 Dice:CE:Hard-L1-ACE ratio. The held-out test set is not accessed until both validation-selected checkpoints have been fixed.

Saved tutorial result

Configuration	Dice	ACE	MCE
Segmentation only	0.799317	0.117391	0.295517
Hard L1-ACE	0.828645	0.090355	0.208534

This corresponds to a 23% reduction in held-out ACE and a 29% reduction in MCE, while Dice increases by 0.0293 for this split and seed. The notebook explicitly presents this as a demonstration rather than a benchmark guarantee.

Validation

• Executed all 36 cells end to end on an NVIDIA RTX 5090
• Passed the repository runner with a one-epoch execution
• Passed targeted notebook schema, syntax, formatting, PEP8, metadata, and output checks
• Confirmed that saved outputs contain no execution errors or private local paths

Dependency

Depends on Project-MONAI/MONAI#9092 for HardL1ACELoss and SoftL1ACELoss.

Summary by CodeRabbit

• Documentation
  • Added a Calibration tutorial using complete 3D MRI volumes from the MSD Task04 Hippocampus dataset.
  • Documented calibration metrics and losses, and the comparison of a baseline with a validation-selected Dice:CE:Hard-L1-ACE configuration on separate held-out test volumes.
  • Added guidance for running the experiment, including a one-epoch CI smoke test, dataset requirements, recommended GPU hardware, and required MONAI APIs.

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Walkthrough

The changes document a 3D segmentation-calibration tutorial and add it to the main tutorial list. The documentation describes the dataset, calibration methods, model comparison, MONAI API requirements, hardware guidance, and CI smoke-test command.

Changes

Calibration tutorial documentation

Layer / File(s)	Summary
Tutorial documentation and index entry calibration/README.md, README.md	The calibration README describes the Task04_Hippocampus dataset, calibration metrics, training comparison, MONAI API requirements, GPU guidance, and CI smoke-test command. The main README links to the tutorial.

Priority: ⬇️ Low

Estimated code review effort: 1 (Trivial) | ~3 minutes

Change: Feature

Suggested reviewers: ericspod

Merge Risk: ⚪ Minimal · up to d3f42

The tutorial link points to the intended notebook. No issue requiring resolution before merge was identified.

Pre-merge checks: 5

Passed checks: 5 passed

Check name	Status	Explanation
Title check		The title clearly and concisely identifies the main change: a 3D segmentation calibration tutorial.
Description check		The description clearly explains the tutorial scope, experiment design, reported results, validation steps, and dependency. It omits the template's Fixes # . line and explicit Checks checklist, but …
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

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Inline comments:
In `@calibration/README.md`:
- Line 12: Update the calibration tutorial documentation near the publication’s
1:1:1 objective to explicitly state that this tutorial uses the
validation-selected 1:1:0.30 Dice:CE:Hard-L1-ACE objective, while retaining the
existing discussion of finite-bin estimates and auxiliary calibration training
limitations.

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• calibration/segmentation_calibration.ipynb

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