Project Grant R01EB038719
- 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...
- This $696,824 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), supports research at the University of North Carolina at Chapel Hill (UNC-CH) to develop robust and interpretable multi-modal AI/ML models for precision medicine. The project aims to address key challenges in integrating complex, heterogeneous biomedical data...
- This National Cancer Institute (CFDA 93.394 Cancer Detection and Diagnosis Research) Project Grant award of $683,089 to the Sloan-Kettering Institute for Cancer Research supports the development of an improved, interpretable deep learning algorithm called DeepLIIF for more reproducible and accurate PD-L1 immunohistochemistry (IHC) biomarker quantification. The goal is to leverage virtual multiplex immunofluorescence (MPIF) restaining and large, diverse datasets across lung and bladder cancers to...
- This $435,827 Project Grant from the National Cancer Institute (NCI) under the CFDA 93.394 Cancer Detection and Diagnosis Research program supports research conducted by New York University (NYU) School of Medicine to develop deep learning methods for analyzing mass spectrometry imaging (MSI) data. The goal is to make MSI data more accessible to existing machine learning workflows by expanding the dimensionality of the data structure to treat each metabolite or lipid as an individual "color...
- This $194,022 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 research led by the Massachusetts General Hospital (MGH) to develop advanced deep learning models for predicting therapeutic response in cancer patients with brain metastases. The grant will fund the candidate, an oncologist at MGH, to build upon his expertise in...
- 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 federal Project Grant award, funded by 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 and validate diffusion-model enabled computational observers for evaluating the performance of deep learning CT image reconstruction and post-processing algorithms. The $428,152 award, which runs from September 1, 2025 to August 31, 2027, will...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $594,891 Project Grant under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" (CFDA 93.286) program to the Illinois Institute of Technology (IIT). The grant aims to develop deep-learning anthropomorphic model observers (AMO) as a substitute for human observers in studies evaluating image quality and diagnostic performance. The project will create annotated image...
- This federal Project Grant award for $653,230.00, provided by 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 MED-SCALLOP - a novel neurosymbolic AI methodology and software tool. The goal is to create inherently explainable AI/ML models for clinical decision support in healthcare, which can effectively integrate deep neural...
- This federal Project Grant award, titled "Automated Multi-Timepoint CT Analysis for Enhanced Metastatic Disease Evaluation in Colorectal Cancer: An Anatomy-Aware Vision-Language AI Approach," was provided by 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). The $718,456 award, with a performance period from July 1, 2025 to May 31, 2029, supports...
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 framework for analyzing mammography data, develop clinically interpretable machine learning architectures for chest CT imaging, and integrate imaging biomarkers with patient data to produce user-friendly risk calculators for early cancer detection and personalized screening. The award was made to Duke University's Office of Research Administration Division and has a performance period from August 1, 2025 to July 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 8/1/25 |