radiology-dataset-guide
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.
Datos de origen
- Repositorio
- aizech/clinical-skills
- Última actividad en el origen
- 21 de abril de 2026 a las 22:11
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 5
- Forks
- 1
Opciones de instalación
De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.
Revisa los archivos de origen
Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.
Explorador de archivos
4 archivosMostrando SKILL.md
SKILL.md
Instrucciones de origen · Vista previa de solo lectura- 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
```
Ver en GitHub