1) Learn how TCIA makes data sharing easier for researchers, and hear a summary of existing datsets that are freely available for download 2) Practice utilizing TCIA for data exploration, cohort definition, and downloading of data 3) Learn how to access public and restricted access datasets using TCIA's REST APIs and other command line tools via Google Colab*Course Description In Person Session Only. On-demand beginning on December 5th.Access to large, high quality data is essential for researchers to understand disease and precision medicine pathways, especially in cancer. However HIPAA constraints make sharing medical images outside an individual institution a complex process. The Cancer Imaging Archive (TCIA) (TCIA) is a free public service funded by the National Cancer Institute which addresses this challenge by providing hosting and de-identification services to take major burdens of data sharing off researchers. TCIA has published over 170 unique data collections containing more than 70 million images. Recognizing that images alone are not enough to conduct meaningful research, most collections are linked to rich supporting data including patient outcomes, treatment information, genomic / proteomic analyses, and expert image analyses (segmentations, annotations, and radiomic / radiogenomic features). This deep learning lab will teach the skills needed to fully access our existing data as well as learn how to submit new data for potential inclusion in TCIA so that it can be used for artificial intelligence research.

In Person Session Only. On-demand beginning on December 5th.Access to large, high quality data is essential for researchers to understand disease and precision medicine pathways, especially in cancer. However HIPAA constraints make sharing medical images outside an individual institution a complex process. The Cancer Imaging Archive (TCIA) (TCIA) is a free public service funded by the National Cancer Institute which addresses this challenge by providing hosting and de-identification services to take major burdens of data sharing off researchers. TCIA has published over 170 unique data collections containing more than 70 million images. Recognizing that images alone are not enough to conduct meaningful research, most collections are linked to rich supporting data including patient outcomes, treatment information, genomic / proteomic analyses, and expert image analyses (segmentations, annotations, and radiomic / radiogenomic features). This deep learning lab will teach the skills needed to fully access our existing data as well as learn how to submit new data for potential inclusion in TCIA so that it can be used for artificial intelligence research.