1. Research Funding
  2. Artificial Intelligence, Machine Learning, and Deep Learning

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Artificial Intelligence, Machine Learning, and Deep Learning

Supports the design and development of artificial intelligence, machine learning, and deep learning to enhance analysis of complex medical images and data.

Emphasis

The emphasis is on development of transformative machine intelligence-based systems, emerging tools, and modern technologies for diagnosing and recommending treatments for a range of diseases and health conditions.  Unsupervised and semi-supervised techniques and methodologies are of particular interest.

Program priorities and areas of interest:

  • clinical decision support systems
  • computer-aided diagnosis
  • computer-aided screening
  • analyzing complex patterns and images
  • screening for diseases
  • natural-language processing and understanding
  • medical decision-making
  • predictive modeling
  • computer vision
  • robotic and image guided surgery
  • personalized imaging and treatment
  • drug discovery
  • radiomics
  • machine/deep learning-based segmentation, registration, etc.

Additional support

This program also supports:

  • early-stage development of software, tools, and reusable convolutional neural networks
  • data reduction, denoising, improving performance (health-promoting apps), and deep-learning based direct image reconstruction
  • approaches that facilitate interoperability among annotations used in image training databases
 

Funded Projects

Grant Number Project Title Principal Investigator Institution
7-R01-EB022911-04 Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization Xi Luo University of Texas Hlth Sci Ctr Houston
5-R01-EB022574-05 Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics Li Shen University of Pennsylvania
5-R21-EB026668-02 Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography Jinyi Qi University of California at Davis
5-R21-EB026061-02 Data-Driven Shape Analysis for Quantitative Severity Stratification in Patients with Metopic Craniosynostosis Ladislav Kavan University of Utah
5-R03-EB027268-02 Deep learning techniques for time-of-flight PET detectors Eric Berg University of California at Davis
5-R01-EB021391-05 Shape Analysis Toolbox for Medical Image Computing Projects Beatriz Paniagua Kitware, Inc.
5-R01-EB006733-10 Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI Pew-Thian Yap Univ of North Carolina Chapel Hill
1-K08-EB027141-01A1 Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures David Dreizin University of Maryland Baltimore
5-K23-EB026493-02 Development of Machine Learning Algorithms to Assess and Train Vesico-Urethral Anastomosis during Robot Assisted Radical Prostatectomy Andrew Hung University of Southern California
5-R01-EB025032-03 Predicting the early childhood outcomes of preterm brain shape abnormalities Natasha Lepore Children'S Hospital of Los Angeles

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