Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics
This Grand Challenge highlighted the need for domain-specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.
Authors
- Kyle J. Myers (ORCID: https://orcid.org/0000-0001-7394-4932)
- Rongping Zeng (ORCID: https://orcid.org/0000-0003-1849-0637)
- Dimitrios S. Gotsis
- Varun A. Kelkar
- Mark A. Anastasio (ORCID: https://orcid.org/0000-0002-3192-4172)
- Frank J. Brooks (ORCID: https://orcid.org/0000-0002-5949-3386)
- Prabhat KC (ORCID: https://orcid.org/0000-0003-3768-1148)
- R. Deshpande (ORCID: https://orcid.org/0000-0001-7582-3712)
Institutions
- Arizona Department of Education (US)
- Center for Devices and Radiological Health (US)
- University of Illinois Urbana-Champaign (US)
- Washington University in St. Louis (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1002/mp.17473
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- U.S. Department of Energy
- American Association of Physicists in Medicine
- National Institutes of Health