EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy
We demonstrated that combining vision-language information in EXACT-Net multi-modal AI framework greatly enhances the performance of vision only models, paving the road to multimodal AI framework for medical image processing.
Authors
- Ziyue Xu (ORCID: https://orcid.org/0000-0002-5728-6869)
- Kathleen M. Kelly (ORCID: https://orcid.org/0000-0002-5120-6266)
- Xue Feng (ORCID: https://orcid.org/0000-0002-2181-9889)
- Kai Ding (ORCID: https://orcid.org/0000-0002-8202-0173)
- Gaofeng Huang (ORCID: https://orcid.org/0009-0009-3837-4650)
- Hamed Hooshangnejad (ORCID: https://orcid.org/0000-0002-1953-0568)
- Quan Chen (ORCID: https://orcid.org/0000-0003-0056-5207)
- Rui Zhang (ORCID: https://orcid.org/0000-0002-3871-1356)
- Yi Luo (ORCID: https://orcid.org/0000-0003-4470-220X)
Institutions
- University of Minnesota (US)
- Johns Hopkins University (US)
- WinnMed (US)
- Nvidia (United States) (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Cancers
- Published
- 2024-12-06
- DOI
- https://doi.org/10.3390/cancers16234097
- Citations
- 5
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- National Institutes of Health
- National Cancer Institute