NSF award 2523787 · The University of Texas Rio Grande Valley

Collaborative Research: CISE Crosscutting Small: SCH: Towards Auto-Prompt Textual Annotations Generation with Domain Knowledge for Multimodal Medical Image Segmentation

Final report due

28 January 2029

in 833 days · The award ends on 30 September 2028.

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PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2028in 703 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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So the final report is due by 28 January 2029.

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Award card

Award number: 2523787
Period of support: 1 Oct 2025 – 30 Sep 2028
Amount: $390,000
Awardee: The University of Texas Rio Grande Valley, Edinburg, TX
Program: HCC-Human-Centered Computing
Directorate: CSE

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…

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