NCI Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence
Artificial intelligence (AI) is advancing. It’s changing the established approaches for how we acquire, study, and understand biomedical imaging data. Scientists (like you) need to continuously develop and refine AI technologies, and this requires easy access to high-quality, diverse, annotated data.
The NCI Cancer Research Data Commons’ Imaging Data Commons (IDC) hosts such data. In this seminar, Dr. Andrey Fedorov from Brigham and Women’s Hospital will address how IDC aims to:
- help refine AI tools by facilitating their development, validation, and clinical translation; and
- create reproducible and transparent AI processing pipelines.
The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.
Dr. Fedorov is part of the Surgical Planning Laboratory at Brigham and Women's Hospital. He is also an associate professor at Harvard Medical School.
Upcoming Events
- AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from HistopathologyMay 07, 2025Co-clinical Imaging Research Resource Program (CIRP) Annual Virtual Meeting 2025—Celebrating A Ten-Year MilestoneMay 07, 2025 - May 08, 2025Workshop Series: Synthetic and Systems Approaches to Interrogate Spatiotemporal Processes in CancerMay 13, 2025 - May 20, 2025Pediatric Preclinical In Vivo Testing (PIVOT) Program: Advancing Targeted Therapies for Childhood CancersMay 13, 2025Agentic AI in Cancer ResearchMay 27, 2025