Queries and downloads large-scale public cancer imaging datasets from the NCI for research and AI training.
This skill provides a specialized interface for Claude to interact with the National Cancer Institute's Imaging Data Commons (IDC). It enables automated discovery and retrieval of high-quality radiology (CT, MR, PET) and pathology data using the idc-index Python package. Researchers and AI developers can execute SQL queries to filter metadata, download DICOM files, and generate visualization URLs without requiring authentication. It is an essential tool for building medical AI training pipelines and conducting large-scale clinical data analysis directly within a development environment.
Características Principales
01SQL-based metadata querying for precise medical dataset selection
02Cross-referencing between imaging, clinical data, and analysis results
03Support for multiple modalities including radiology and slide microscopy
04Browser-based visualization links for medical imaging series
05Direct DICOM file downloading and automated organization
060 GitHub stars
Casos de Uso
01Visualizing and verifying public radiology collections prior to local storage
02Building curated training datasets for medical imaging AI models
03Conducting large-scale retrospective cancer research and data mining
