dataset-preprocessing

name	dataset-preprocessing
description	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.

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 format
  • dicom - DICOM files
  • nifti - NIfTI volumes
  • metadata - Header/excel data
  • mixed - Multiple formats
• task_type (required): Downstream ML task
  • detection - Object/bounding box detection
  • segmentation - Pixel-level segmentation
  • classification - Image classification
  • regression - Continuous value prediction
• modality (optional): Imaging modality
• multi_vendor (optional): Boolean for multi-site/multi-vendor data
• dataset_scale (optional): Small (<1K), medium (1K-100K), large (>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: large