MD-SA2: optimizing Segment Anything 2 for multimodal, depth-aware brain tumor segmentation in sub-Saharan populations
MD-SA2 displays strong potential for supporting the diagnosis and treatment planning of brain tumors. It may contribute to narrowing health inequities, especially in medically underserved areas where data quantity and quality limitations reduce the efficacy of traditional automated approaches.
Authors
- Benjamin Li (ORCID: https://orcid.org/0000-0002-1783-7013)
- Dimah Dera (ORCID: https://orcid.org/0000-0002-7168-5858)
- Kai Ding (ORCID: https://orcid.org/0000-0002-8202-0173)
Institutions
- Johns Hopkins University (US)
- Rochester Institute of Technology (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1117/1.jmi.12.2.024007
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84