Project Grant R01EB036530
- This $681,215 federal Project Grant was awarded on September 1, 2025 by the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to New York University (NYU) to develop AI-powered diagnostic tools for early prostate cancer detection. The project aims to leverage machine learning models to infer the presence of clinically significant prostate cancer using a minimal amount of degraded MRI data, in order to enable widespread, cost-effective population-level disease...
- This $300,000 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports the development of interpretable machine learning tools to uncover imaging biomarkers associated with breast and lung cancer. The project aims to create dimension reduction techniques to visualize features from deep learning models, establish an interpretable...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award (CFDA 47.070) of $195,000 will fund the development of a weakly-supervised breast cancer detection system using active and weakly-supervised learning techniques to improve breast cancer diagnosis from ultrasound images. The project aims to create a deep learning model that can detect breast cancer effectively with minimal annotations, addressing the challenges of noisy, low-contrast...
- The National Cancer Institute (NCI) has awarded a $572,490 Project Grant to The Regents of the University of California, San Francisco (UCSF) under the federal Cancer Detection and Diagnosis Research program (CFDA 93.394). The project, titled "PILLAR: Multi-Modal Imaging AI Models for Breast Cancer Risk", aims to develop an AI-powered tool to predict breast cancer risk using longitudinal data from mammograms, tomosynthesis, and MRI scans. The goal is to create more accurate cancer risk...
- This $1,000,000 Project Grant was awarded on August 1, 2025 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The funding supports the development of a novel Artificial Intelligence (AI) and Machine Learning (ML) framework to reduce health outcome variability for breast cancer, particularly among Black women who experience disproportionately higher mortality rates. The key products and services to be delivered...
- The National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded a $500,000 Project Grant to the Massachusetts Institute of Technology (MIT) to develop a wearable, real-time 3D ultrasound system for breast cancer screening. The objective is to improve access to early breast cancer detection, especially for individuals with dense breast tissue or limited imaging access, by providing a low-power, wide-angle, high-resolution volumetric imaging system as an adjunct to routine...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, with the CFDA number 47.070, provides $203,981 in funding to Kean University to develop a weakly-supervised breast cancer detection system using active and weakly-supervised learning techniques applied to breast ultrasound imaging. The project aims to create a deep learning model that can effectively detect breast cancer in ultrasound images with minimal reliance on costly manual...
- This Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) aims to develop advanced deep learning algorithms to enhance the management of multiple myeloma (MM), a type of hematologic cancer. The $755,336 award, with a period of performance from August 1, 2025 to June 30, 2029, will focus on two key objectives: 1) Developing automated...
- This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $371,161 to develop advanced AI-based risk models for cervical cancer screening in low-resource settings. The project aims to leverage time-series imaging data and self-supervised learning to enhance the diagnostic accuracy of automated visual evaluation (AVE) models. The goal is to create a comprehensive risk stratification system that combines imaging data, HPV...
- The National Cancer Institute (NCI) awarded a $700,611 Project Grant under the Cancer Cause and Prevention Research program (CFDA 93.393) to the Sloan-Kettering Institute for Cancer Research. The grant, titled "Practical Randomized Controlled Trial of Artificial Intelligence for Melanoma Diagnosis (PRACTA-MEL)," aims to determine the benefits and barriers to clinical adoption of AI systems for improving melanoma detection and reducing the number of unnecessary biopsies. The project...
This federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), aims to improve the diagnostic accuracy and cost-effectiveness of breast cancer screening through the development of an AI system. The $672,087 grant, awarded on August 12, 2025, will fund a research project at New York University School of Medicine to build a multi-modal deep neural network, "Multi-Modal Diagnoser (MMD)", that integrates mammographic and ultrasound imaging data to enhance breast cancer detection. The researchers will also develop an "Ultrasound Benefit Predictor (UBP)" model to determine the necessity of supplemental screening ultrasounds for women with dense breasts, with the goal of reducing unnecessary diagnostic workups and biopsies. The project is expected to be completed by May 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $672.1k | 8/12/25 |