Add missing channel_wise parameter to RandScaleIntensityFixedMean

[engmohamedsalah]

Summary

• Adds the channel_wise parameter to RandScaleIntensityFixedMean and RandScaleIntensityFixedMeand, which was documented in docstrings but never implemented.
• When channel_wise=True, a separate random scale factor is generated per channel, and preserve_range/fixed_mean are applied per channel — following the existing pattern from RandScaleIntensity.
• Fixes docstring indentation for channel_wise in both array and dictionary transforms.

Fixes #8363

Changes

monai/transforms/intensity/array.py

• Added channel_wise parameter to RandScaleIntensityFixedMean.__init__
• Updated randomize() to generate per-channel factors when channel_wise=True
• Updated __call__ to apply per-channel scaling with individual random factors

monai/transforms/intensity/dictionary.py

• Added channel_wise parameter to RandScaleIntensityFixedMeand.__init__
• Updated __call__ to pass image data to randomize() (needed for channel count), following the pattern from RandScaleIntensityd

Tests

• Added test_channel_wise and test_channel_wise_preserve_range to test_rand_scale_intensity_fixed_mean.py
• Added test_channel_wise to test_rand_scale_intensity_fixed_meand.py

Test plan

• All existing tests pass (no regressions)
• New channel_wise tests pass for both array and dictionary transforms
• Verified per-channel mean preservation with fixed_mean=True
• Verified preserve_range clipping works per channel

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📥 Commits

Reviewing files that changed from the base of the PR and between ae90682 and 3d83082.

📒 Files selected for processing (1)

• monai/transforms/intensity/array.py

🚧 Files skipped from review as they are similar to previous changes (1)

• monai/transforms/intensity/array.py

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📝 Walkthrough

Walkthrough

Adds channel-wise support to RandScaleIntensityFixedMean (array) and its dictionary wrapper. The array transform gains a new channel_wise: bool parameter, stores it, forwards it to the internal scaler, samples per-channel factors when enabled, and applies per-channel scaling by iterating channels and concatenating results while preserving dtype, fixed_mean, and preserve_range semantics. The dictionary transform propagates channel_wise into its internal scaler, changes randomization to self.scaler.randomize(d[first_key]) when keys share the same factor, and adds an early-return path that converts all values to tensors when first_key == (). Tests were added to validate per-channel scaling and preserve_range behavior.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 27.27% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title directly describes the main change: adding a previously missing channel_wise parameter to RandScaleIntensityFixedMean.
Description check	✅ Passed	Description covers all required sections: summary, changes, tests, and test plan. Fixes issue reference included.
Linked Issues check	✅ Passed	PR implements all objectives from #8363: adds channel_wise parameter [#8363], generates per-channel factors [#8363], applies per-channel scaling [#8363], ensures fixed_mean and preserve_range work per-channel [#8363], follows RandScaleIntensity pattern [#8363].
Out of Scope Changes check	✅ Passed	All changes align with #8363 scope: parameter implementation, per-channel logic, docstring corrections, and corresponding tests.

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[ericspod]

Hi @engmohamedsalah I made a few comments below but it otherwise looks good to me with a few changes. Thanks!

[engmohamedsalah]

@ericspod please check again after apply all your comments