agentsclimarketplace

Radiology dataset guide

Skill aizech/clinical-skills/.continue/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

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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

2.4 KB, as published. Nobody here has run it

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

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.