PeopleofSuperintelligence

Researcher

Justin Kirby

Also known as Justin S. Kirby

Justin Kirby is a cancer imaging informatics specialist whose team at the Frederick National Laboratory for Cancer Research founded The Cancer Imaging Archive (TCIA), a public archive of cancer images that researchers use to develop AI tools. After an information technology degree at Duquesne University, he joined the laboratory in 2008 to support the National Cancer Institute's Cancer Imaging Program, and his team launched TCIA in 2011. He coauthored the 2013 paper on running TCIA, two 2017 Scientific Data papers on its public collections and on glioma segmentation labels, and a 2018 study of brain tumor segmentation algorithms from the BraTS challenge. In 2021 he oversaw TCIA data curation as the archive took on COVID-19 imaging data sets, and he was named among the Project MONAI advisory board and working group leads. In March 2025 he announced new search filters for the TCIA Radiology Portal, and in July 2025 he posted the notice that TCIA would stop hosting controlled-access datasets.

Now at
National Cancer Institute

Timeline

  1. 2008

    Joining Frederick National Laboratory

    He earns an undergraduate degree in information technology at Duquesne University in Pittsburgh. In 2008, he joins the Frederick National Laboratory for Cancer Research to support the National Cancer Institute's Cancer Imaging Program, where he works on image de-identification and on reproducibility in cancer imaging research.

  2. 2011–2013

    Founding The Cancer Imaging Archive

    His team founds The Cancer Imaging Archive (TCIA) in 2011. In 2013, he coauthors the Journal of Digital Imaging paper on maintaining and operating TCIA, which reports that the archive accumulated 23 collections and 3.3 million images in its first year.

  3. 2017

    Public collections and glioma labels

    In 2017, he coauthors two Scientific Data papers. One reviews the public radiology collections of TCIA, which hold 30.9 million images from about 37,568 subjects; the other releases segmentation labels and radiomic features for 243 pre-operative glioma MRI scans from The Cancer Genome Atlas collections, intended to support the evaluation of computer-aided segmentation methods.

  4. 2018

    A BraTS challenge study

    In 2018, he coauthors a study of the machine learning methods entered in the 2012–2018 International Brain Tumor Segmentation (BraTS) challenges. The study assesses glioma sub-region segmentation, tumor progression and overall-survival prediction on multi-parametric MRI scans.

  5. 2021

    COVID-19 data on TCIA

    As a project manager in Frederick National Laboratory's Applied and Developmental Research Directorate, he oversees TCIA data curation while the archive works on four COVID-19 imaging data sets in 2021. Researchers use one of these data sets to train artificial intelligence that detects COVID-19 pneumonia on chest CT scans with 90.8% accuracy.

  6. 2021–2022

    Project MONAI advisory board

    In 2021, he is named among the advisory board and working group leads of Project MONAI. In 2022, he is a coauthor of the paper that introduces MONAI as a freely available, PyTorch-based framework for deep learning in healthcare.

  7. March 2025

    New Radiology Portal search filters

    In March 2025, he announces new features in the NBIA software that powers the TCIA Radiology Portal. The redesigned Search tab groups filters into General, Patients and Images categories and adds filters such as patient sex, patient age, slice thickness and pixel spacing.

  8. July 2025

    Ending controlled-access hosting

    In July 2025, he posts on the TCIA site that, to comply with a new NIH policy on controlled-access data, TCIA ends hosting of controlled-access datasets that month. Datasets with head images get defaced or masked open-access versions, and datasets linked to the NCTN Data Archive move to NCI's clinical data commons.

Career

  1. From 2008National Cancer InstituteProject Manager

Education

  1. Duquesne UniversityUndergraduate degree in information technology

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes National Institutes of Health. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Duquesne University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  4. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Justin Kirby yet. Send us a free-licensed photo.

  1. AIMI-IBIIS Seminar: Justin Kirby - The Cancer Imaging Archive: Addressing the Cancer Imaging Community's Data Sharing Needs aimi.stanford.edu. Checked 21 October 2026.
  2. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  3. The public cancer radiology imaging collections of The Cancer Imaging Archive nature.com. Checked 21 October 2026.
  4. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features nature.com. Checked 21 October 2026.
  5. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge arxiv.org. Checked 21 October 2026.
  6. The Cancer Imaging Archive posts COVID-19 imaging data to benefit community frederick.cancer.gov. Checked 21 October 2026.
  7. MICCAI 2021 Daily – Thursday rsipvision.com. Checked 21 October 2026.
  8. MONAI: An open-source framework for deep learning in healthcare arxiv.org. Checked 21 October 2026.
  9. New search features added to our Radiology Portal - The Cancer Imaging Archive (TCIA) cancerimagingarchive.net. Checked 21 October 2026.
  10. New NIH Policies for Controlled Access Data - The Cancer Imaging Archive (TCIA) cancerimagingarchive.net. Checked 21 October 2026.