Add RankSEG integration tutorial

[statmlben]

Related to MONAI-#8908

Description

This PR adds a runnable tutorial demonstrating how to use RankSEG as an optional third-party post-processing transform in a MONAI workflow.

The tutorial:

• uses the pretrained pancreas_ct_dints_segmentation MONAI Bundle and a real MSD Task07 Pancreas case;
• compares conventional argmax decoding with RankSEG using per-class Dice scores and a visualization;
• demonstrates the array-based RankSEG transform;
• demonstrates the dictionary-based RankSEGd transform in a MONAI Compose post-processing pipeline;
• documents the public dataset source, CC BY-SA 4.0 license, and research-use context.

This PR also adds the tutorial to the repository README and registers it in doesnt_contain_max_epochs because it is an inference-only notebook.

Checks

• Avoid including large-size files in the PR.
• Clean up long text outputs from code cells in the notebook.
• Check the contents and remove sensitive information such as user names and private keys.
• Verify hyperlinks, markdown content, and relative repository paths.
• Notebook runs automatically with:

./runner.sh -t modules/rankseg_integration.ipynb

Results

Argmax macro Dice: 0.7747
RankSEG macro Dice: 0.8502
Paired Dice change: +0.0755
Pancreas Dice: 0.8764 -> 0.8898
Tumor Dice: 0.6730 -> 0.8106

Summary by CodeRabbit

• New Features

  • Added a tutorial demonstrating optional RankSEG/RankSEGd post-processing with MONAI segmentation workflows.
  • Compares RankSEG decoding with standard argmax predictions using a pretrained pancreas segmentation model.
  • Includes Dice score evaluation, visual comparisons, and examples for both tensor and dictionary-based workflows.

• Documentation

  • Added the RankSEG integration notebook to the documented list of available modules and examples.

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Walkthrough

Adds a MONAI tutorial integrating RankSEG and RankSEGd with a pretrained pancreas DiNTS model, comparing RankSEG against argmax using Dice scores and visualizations. It also documents the notebook and exempts it from the runner’s max_epochs check.

Changes

RankSEG integration tutorial

Layer / File(s)	Summary
RankSEG transform wrappers modules/rankseg_integration.ipynb	Introduces RankSEG for multiclass probability-map decoding and documents configuration and expected inputs.
Pancreas model inference modules/rankseg_integration.ipynb	Downloads and preprocesses a pancreas case, loads the MONAI DiNTS bundle, and runs sliding-window inference.
Decoder comparison and evaluation modules/rankseg_integration.ipynb	Compares argmax and RankSEG predictions, calculates foreground Dice scores, and visualizes tumor-region differences.
Dictionary integration and execution support modules/rankseg_integration.ipynb, README.md, runner.sh	Demonstrates RankSEGd, cleans temporary data, documents the notebook, and adds its runner exemption.

Estimated code review effort: 3 (Moderate) | ~25 minutes

Sequence Diagram(s)

sequenceDiagram
  participant Notebook
  participant DiNTS
  participant RankSEG
  participant DiceMetric
  Notebook->>DiNTS: run sliding-window inference
  DiNTS-->>Notebook: return segmentation logits
  Notebook->>RankSEG: decode softmax probabilities
  RankSEG-->>Notebook: return class-index prediction
  Notebook->>DiceMetric: compare prediction with foreground labels
  DiceMetric-->>Notebook: return Dice scores

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Possibly related PRs

• Project-MONAI/tutorials#2065: Updates the same runner.sh notebook exemption list.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely summarizes the main change: adding a RankSEG integration tutorial.
Description check	✅ Passed	The description follows the template well, with a summary, checklist, results, and relevant links/context.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 2

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Inline comments:
In `@modules/rankseg_integration.ipynb`:
- Line 47: Update the dependency-check command in the notebook setup cell to
validate both MONAI and nibabel imports before skipping installation. Ensure any
installation runs through the active kernel interpreter, and retain the existing
MONAI extras including nibabel and tqdm so the later LoadImaged call can read
.nii.gz files.
- Around line 630-653: Update both torch.load calls for the architecture and
checkpoint artifacts in the model-loading flow to use weights_only=True instead
of explicitly enabling full pickle deserialization. Preserve the existing paths
and map_location settings, and only retain weights_only=False if the loaded
files require trusted non-tensor objects.

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Fix all unresolved CodeRabbit comments on this PR:

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⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: e68feaab-cd4b-4ade-99e2-419988bdcf7c

📥 Commits

Reviewing files that changed from the base of the PR and between 81dcf0f and 13eed5c.

📒 Files selected for processing (3)

• README.md
• modules/rankseg_integration.ipynb
• runner.sh

[ericspod]

Hi @statmlben thanks for this tutorial as we had discussed. I think it's fine overall for the existing content, but we need a discussion on what Rankseg is and how the algorithm works. At the top you should describe what it is, how it differs from other components, what it's used for, what the implications of using it are, etc. This by no means needs the same details as in your paper but should be motivating enough so readers know what the notebook is demonstrating and whether they'd want to use it themselves or not. I think the Coderabbit comments should be looked at but I had responses here as well.

[statmlben]

Thanks @ericspod for the feedback. I added an introductory section near the top of the notebook explaining what RankSEG is, how it differs from argmax, fixed thresholding, and morphology-based post-processing, and how the RMA workflow operates at a high level. The section also discusses suitable use cases, probability requirements, computational cost, and the need to validate improvements on representative data.

I also addressed your inline suggestions by consolidating the dependency installation command and using the Bundle ConfigParser to construct the model.