imaging-data-commons

You can search for NCI cancer imaging datasets by querying the Imaging Data Commons to filter DICOM collections specifically by modality, anatomical site, or cancer type. This allows researchers to find relevant radiology and pathology images for analysis.

Yes, you can query DICOM metadata at scale using SQL via Google Cloud BigQuery. This integration enables large-scale metadata queries across the NCI Imaging Data Commons repository without requiring local storage for the entire dataset.

You can download specific DICOM series directly from Google Cloud Storage or by using the IDAT tool. This enables researchers to acquire curated subsets of SeriesInstanceUIDs for model training and analysis.

To programmatically access NCI cancer imaging data, you need the idc-index Python library along with google-cloud-bigquery, requests, and pandas. These dependencies facilitate searching collections and querying metadata.

Yes, you can find CT scans of lung cancer patients for detection models by searching the Imaging Data Commons for relevant collections, filtering for CT scans, and downloading a curated subset of SeriesInstanceUIDs for training.

The best way to access radiology and pathology images without local storage is by querying the NCI Imaging Data Commons hosted on Google Cloud. This approach enables searching and downloading specific DICOM collections on demand.