Dataset Preprocessing
Provides preprocessing pipelines and techniques for radiology datasets used in AI development. Use when user mentions "preprocess radiology data", "DICOM preprocessing", "image normalization", "data augmentation", or needs to prepare datasets.
Install
$ agentstack add skill-aizech-clinical-skills-dataset-preprocessing ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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Dataset Preprocessing Skill
Triggers
- "preprocess radiology data"
- "DICOM preprocessing"
- "image normalization"
- "data augmentation"
- "quality control pipeline"
- "mask generation"
- "multi-site harmonization"
- "training data preparation"
Parameters
input_format(required): Source data formatdicom- DICOM filesnifti- NIfTI volumesmetadata- Header/excel datamixed- Multiple formatstask_type(required): Downstream ML taskdetection- Object/bounding box detectionsegmentation- Pixel-level segmentationclassification- Image classificationregression- Continuous value predictionmodality(optional): Imaging modalitymulti_vendor(optional): Boolean for multi-site/multi-vendor datadataset_scale(optional): Small (100K)
Preprocessing Components
Image Processing
- Intensity normalization (z-score, min-max, percentile-based)
- Windowing/leveling for CT/MRI
- Resampling to isotropic voxel size
- Brain extraction (skull stripping)
- Bias field correction for MRI
Quality Control
- Automated quality scoring
- Artifact detection
- Contrast-to-noise ratio
- Resolution verification
- Human-in-the-loop review for edge cases
Augmentation
- Geometric: rotation, flip, scale, elastic deformation
- Intensity: noise, contrast, brightness
- Modality-specific: CT windowing variants, MRI sequence mixing
- Generative: synthetic data augmentation
Format Conversion
- DICOM to NumPy/PyTorch/TensorFlow
- DICOM to NIfTI for volumetric data
- Annotation format conversion (CSV, COCO, YOLO, Pascal VOC)
Output Format
Returns structured JSON with:
- Processing pipeline steps
- Code snippets for each transformation
- Validation checks and statistics
- Expected output specifications
- Common pitfalls and mitigations
Usage Examples
input_format: dicom
task_type: detection
modality: CT
multi_vendor: true
input_format: nifti
task_type: segmentation
dataset_scale: largeSource & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: aizech
- Source: aizech/clinical-skills
- License: MIT
- Homepage: https://www.corpusanalytica.com/skills/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.