Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks
) coregistered maps of cellular and vascular structures, revealing distinct microscopic patterns associated with anatomic structures and precancerous lesions. Our results suggest the potential of this computational endomicroscope to address the unmet need for early cancer detection at the point of care.
Authors
- Vivek Boominathan (ORCID: https://orcid.org/0000-0003-4875-3135)
- Jimin Wu (ORCID: https://orcid.org/0000-0001-6383-9020)
- Richard A. Schwarz
- Huayu Hou (ORCID: https://orcid.org/0000-0003-3996-0327)
- Rebecca Richards‐Kortum (ORCID: https://orcid.org/0000-0003-2347-9467)
- Tomasz Tkaczyk (ORCID: https://orcid.org/0000-0001-7153-3312)
- Jinyun Liu (ORCID: https://orcid.org/0000-0001-6619-3886)
- Karthik Goli
- Mila P. Salcedo
- Ashok Veeraraghavan (ORCID: https://orcid.org/0000-0001-5043-7460)
- Jennifer Carns
- Ann M. Gillenwater
- Argaja Shende
- Kathleen M. Schmeler
- Preetha Ramalingam
- Jacob T. Robinson (ORCID: https://orcid.org/0000-0002-3509-3054)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- Baylor College of Medicine (US)
- Rice University (US)
Publication Details
- Journal
- Proceedings of the National Academy of Sciences
- Published
- 2026-05-11
- DOI
- https://doi.org/10.1073/pnas.2602705123
- Primary Topic
- Advanced Fluorescence Microscopy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- National Cancer Institute
- National Institute of Dental and Craniofacial Research