Project Grant 2523786
- This federal Project Grant award, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports the development of an automated framework to generate textual annotations for medical image segmentation without reliance on manual prompts. The $390,000 award to the University of Texas Rio Grande Valley (UTRGV) aims to create robust and generalizable artificial intelligence tools for clinical applications. Key components...
- This National Science Foundation (NSF) Project Grant award, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $855,000 in funding to the Texas A&M Engineering Experiment Station (Tees) to develop a bimodal interpretable multi-instance medical image classification framework. The research aims to create a more scalable, interpretable, and robust artificial intelligence (AI) system to better analyze complex medical images, such as from multiple patient...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $220,000 to Yale University from September 1, 2023 through August 31, 2027. The project aims to develop a smarter artificial intelligence (AI) system to better understand and analyze complex medical images, such as those from multiple scans of a patient. The research team will tackle challenges to make the AI system more scalable, interpretable,...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $499,997 to Boston University to develop new methods for making AI models in healthcare more transparent, adjustable, and reliable. The project aims to create tools that can help clinicians and researchers understand, diagnose, and correct errors in AI systems used for disease diagnosis from medical images like CT scans and mammograms. The research...
- This Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program provides $305,000 in funding to Segmedix, CO. to develop an AI-powered software system for standardizing and analyzing prostate MRI scans. The goal is to improve the accuracy, consistency, and efficiency of prostate cancer diagnosis by creating software that can harmonize MRI images from different scanners and manufacturers, and provide precise 3D outlines of the...
- This $255,807 National Science Foundation project grant will fund the development of an AI-assisted software system to accelerate the labeling of medical tomographic images. Administered through the NSF Directorate for Engineering's Engineering program (CFDA 47.041), the grant aims to extract new information from medical images and improve patient outcomes. Alienbyte Scientific Software Inc. will apply machine learning algorithms to create an adaptive system that evolves to increase the speed,...
- This EAGER (Early-concept Grants for Exploratory Research) Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $200,000.00 to the University of Virginia from October 1, 2025 to September 30, 2027. The project aims to develop concept-based reasoning approaches for improving the interpretability and accountability of deep neural networks and large pre-trained language models in healthcare...
- This $170,000 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop robust and human-aligned deep learning techniques for analyzing medical sensor time-series data. The primary goals are to: 1) identify input confounders that lead to spurious correlations in time-series data, 2) design knowledge-editing strategies to correct these spurious correlations, and 3) investigate the techniques...
- This $1,000,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop a novel multi-modal transfer learning framework to enhance health outcomes for breast cancer patients. The project, led by the University of Nebraska Medical Center, will integrate multi-omics data and establish advanced machine learning techniques to improve the generalizability and performance of AI models for breast cancer...
- This Project Grant award from the National Science Foundation (NSF) under the Integrative Activities program (CFDA 47.083) will support the development of novel deep generative models and algorithms for analyzing and visualizing scientific texts. The $224,036 award to New Mexico State University (NMSU) will fund a fellowship for an assistant professor and graduate student training. The project aims to create algorithms that model the intent, topics, and embeddings within scientific documents, as...
This Project Grant award for $210,000 was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The project, led by Texas Woman's University (TWU), aims to develop an automated framework that generates segmentation-specific textual descriptions for medical image analysis without the need for manual prompts. The key components include: This research project seeks to create robust and generalizable AI tools for real-world clinical use by leveraging the bidirectional relationship between images and text. The broader impacts include engaging students through hands-on research, enabling interdisciplinary collaboration, and delivering open-access tools that advance science, education, and clinical relevance through close collaboration with medical experts.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $210.0k | 8/19/25 |