This Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) to Emory University for $140,511 supports the development of a computational framework that leverages AI visual explanation to guide AI-based abdominal cancer diagnostic imaging. The key objectives are to: 1) Improve AI sample efficiency through visual explanation supervision of cancer imaging annotations, 2) Consolidate AI's knowledge across multi-institutional data while...
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 Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) provides $591,606.00 to the University of California, Los Angeles (UCLA) to conduct research on optimizing the human-computer interaction in pathology and understanding the impact of computer-aided diagnosis (CAD) tools on pathologists' interpretive performance. The key products and services to be delivered under this 5-year award include: Randomizing 250 pathologists to examine the...
This $249,000 federal Project Grant award from the National Institute of Environmental Health Sciences (NIEHS) under the Medical Library Assistance (CFDA 93.879) program supports the development of a novel Contrastive Feature Analysis (CFA) framework for reliable visualization and effective design of high-performance deep neural networks (DNNs) for medical image analysis. The key objectives of this 3-year project are to: 1) develop an efficient CFA visualization technique for high-dimensional...
This $1,199,514 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) aims to develop a comprehensive and scalable risk prediction model, termed "PRECISE", that fuses imaging and non-imaging data to enable early detection of pancreatic ductal adenocarcinoma (PDAC) in asymptomatic individuals. The key products and services to be delivered include: Development of deep learning models to segment imaging biomarkers from abdominal...
This National Cancer Institute (NCI) Project Grant under CFDA 93.394 "Cancer Detection and Diagnosis Research" program will fund the development of a virtual multiplex immunofluorescence (MPIF) restaining algorithm, DeepLIIF, to improve the reproducibility and accuracy of PD-L1 immunohistochemistry (IHC) biomarker quantification. The $683,089 award to the Sloan-Kettering Institute for Cancer Research aims to incorporate MPIF immune cell markers, whole-cell segmentation, and large...
This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Yale University totals $167,500 and will be executed from September 2023 through August 2025. The primary objectives are to: Develop deep neural network-based emulation analysis methods and software to objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer prognosis, especially when randomized clinical trials are not feasible. The...
This Project Grant award from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), provides $1,625,000.00 to Onc.ai, Inc. to develop and validate a radiomics-based multi-modal predictive model for metastatic non-small cell lung cancer patients treated with PD-1 immunotherapy. The key objectives are to: 1) Validate the predictive models in a multi-institutional prospective clinical study, 2) Evaluate the performance characteristics and...
This federal Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $245,087 to the University of Delaware to develop an advanced AI-powered system for interpreting prostate MRI scans. The key products and services to be delivered include: Curation of a comprehensive dataset by Memorial Sloan Kettering radiologists, who will annotate 300 public and 50 proprietary MRI scans with corresponding standardized PIRADS radiologist reports....
This $968,999 federal Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) will support a study to optimize imaging-based surveillance for lung cancer survivors treated with curative-intent surgery. The primary objectives are to: 1) determine real-world patterns and effectiveness of imaging surveillance, 2) develop and validate risk prediction models for lung cancer recurrence and second primary lung cancer using imaging biomarkers from...