Collaborative Research: CISE Crosscutting Small: SCH: Towards Auto-Prompt Textual Annotati — NSF Award to Texas Woman's University
Medical image segmentation is essential for clinical decision making and disease monitoring, yet current deep-learning approaches are limited by their reliance on imaging data alone and lack of contextual understanding. Vision-language models (VLMs) offer a promising alternative by generating textual annotations, but t
| Award title | Collaborative Research: CISE Crosscutting Small: SCH: Towards Auto-Prompt Textual Annotati |
|---|---|
| Award ID | 2523786 |
| Awardee | Texas Woman's University |
| City | DENTON |
| State | TX |
| Amount obligated | $210,000 |
| Principal investigator | Islam Ebeid |
| Program | HCC-Human-Centered Computing |
| Start date | 10/01/2025 |
| Abstract | Medical image segmentation is essential for clinical decision making and disease monitoring, yet current deep-learning approaches are limited by their reliance on imaging data alone and lack of contextual understanding. Vision-language models (VLMs) offer a promising alternative by generating textual annotations, but their dependence on manually crafted prompts and poor adaptation to segmentation tasks constrain their clinical utility. Moreover, these models struggle to generalize across imaging |
| Source | NSF Awards |
$799/mo
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