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Radiology dataset guide

Skill aizech/clinical-skills/skills/radiology-dataset-guide

A collection of AI agent skills focused on medical imaging and healthcare workflows. Built for radiologists, healthcare IT professionals, and researchers who want AI coding agents to help with imaging workflows, clinical documentation, AI integration, and medical research. Works with Claude Code, Codex, Cursor, Windsurf, and many other agents.

Install
npx -y skills add aizech/clinical-skills --skill 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.

SKILL.md

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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

DatasetModalityPrimary TaskAccessAnnotations
RSNA Bone AgeX-rayRegressionPublicAge, quality
RSNA PneumoniaChest X-rayDetectionPublicBounding boxes
RSNA Brain HemorrhageCTDetectionPublicBounding boxes, type
NIH ChestX-ray14Chest X-rayClassificationPublicLabels
CheXpertChest X-rayClassificationInstitutionalLabels
MIMIC-CXRChest X-rayMultiPhysioNetLabels, reports
CheXphotoChest X-rayClassificationPublicSynth/real pairs
LUNA16CTDetectionPublicNodule centers
KiTSCTSegmentationPublicKidney/tumor
BraTSMRISegmentationResearchMulti-modal seg
PANDAHistologyClassificationPublicBiopsy grades
OBJ-CXRChest X-rayDetectionPublicBounding 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

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