radiology-dataset-guide

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Guides researchers and developers through radiology dataset selection, access, and utilization for AI development. Use when user mentions "radiology dataset", "medical imaging data", "RSNA challenge", "MIMIC data access", or needs dataset guidance.

aizech By aizech schedule Updated 4/21/2026
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name: radiology-dataset-guide description: Guides researchers and developers through radiology dataset selection, access, and utilization for AI development. Use when user mentions "radiology dataset", "medical imaging data", "RSNA challenge", "MIMIC data access", or needs dataset guidance.

Radiology Dataset Guide Skill

Triggers

  • "radiology dataset"
  • "medical imaging data"
  • "RSNA challenge"
  • "MIMIC data access"
  • "CheXpert download"
  • "dataset comparison"
  • "training data preparation"
  • "public dataset"

Parameters

  • task_type (required): ML/AI task being solved
    • detection - Abnormality/nodule/cancer detection
    • segmentation - Organ or lesion segmentation
    • classification - Disease or finding classification
    • reconstruction - Image reconstruction/enhancement
    • quantification - Measurement and feature extraction
  • anatomy (optional): Body region or organ system
  • modality (optional): Imaging modality preference
  • access_requirements (optional): Data use restrictions
  • commercial_use (optional): Boolean for commercial application intent

Dataset Inventory

Dataset Modality Primary Task Access Annotations
RSNA Bone Age X-ray Regression Public Age, quality
RSNA Pneumonia Chest X-ray Detection Public Bounding boxes
RSNA Brain Hemorrhage CT Detection Public Bounding boxes, type
NIH ChestX-ray14 Chest X-ray Classification Public Labels
CheXpert Chest X-ray Classification Institutional Labels
MIMIC-CXR Chest X-ray Multi PhysioNet Labels, reports
CheXphoto Chest X-ray Classification Public Synth/real pairs
LUNA16 CT Detection Public Nodule centers
KiTS CT Segmentation Public Kidney/tumor
BraTS MRI Segmentation Research Multi-modal seg
PANDA Histology Classification Public Biopsy grades
OBJ-CXR Chest X-ray Detection Public Bounding boxes

Output Format

Returns structured JSON with:

  • Relevant datasets ranked by suitability
  • Annotation quality and completeness
  • Access procedure and requirements
  • Key publications and benchmarks
  • Preprocessing recommendations
  • Compliance and ethics considerations

Usage Examples

task_type: detection
anatomy: lung
modality: CT

task_type: classification
anatomy: chest
commercial_use: true
Install via CLI
npx skills add https://github.com/aizech/clinical-skills --skill radiology-dataset-guide
Repository Details
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Occupations
Medical Scientists Except Epidemiologists
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