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There are a series of notebooks which demonstrate how to access and work with TCIA datasets using Python and our REST APIs. Most of them heavily leverage functionality from tcia_utils, which is a Python package that aims to provide functions to make it easier to work with TCIA datasets.

Guidance on sharing and using datasets related to Machine Learning or Artificial Intelligence studies on TCIA

In the case of "radiomics" and other quantitative imaging features it is critical to use standardized image feature definitions such as those outlined in this publication.  

Radiology: Artificial Intelligence has initiated a collection of articles to address challenges of bias in medical imaging AI systems which we recommend researchers keep in mind when publishing or using datasets on TCIA.

Information about deep Deep Learning parameters are also critical necessary for researchers to reproduce Deep Learning experiments. Where applicable, we recommend that data submitters include the following key pieces of information in their dataset summaries such that TCIA users can easily reproduce their study and compare their analysis results.