Artificial Intelligence, Machine Learning, and Deep Learning

This program supports the design and development of intelligent and innovative algorithms, software, methods, and computational tools to enhance analysis of complex medical images and data. Relevant technologies include those that facilitate organization, representation, retrieval, analysis, recognition, and classification of biomedical and biological data and images.


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
Grant Number Project Title Principal Investigator Institution
5-R01-EB020683-04 Quantitative Image Modeling for Brain Tumor Analysis and Tracking Khan Iftekharuddin Old Dominion University
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-U01-EB023822-03 Software tool for routine, rapid, patient-specific CT organ dose estimation Taly Schmidt Marquette University
5-R01-EB022573-04 Pattern Analysis of fMRI via machine learning/sparse models: application to brain development Christos Davatzikos University of Pennsylvania
5-R01-EB022574-05 Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics Li Shen University of Pennsylvania
1-R01-EB028591-01 Quantitative, non-invasive characterization of urinary stone composition and fragility using multi-energy CT and machine learning techniques Cynthia Mccollough Mayo Clinic Rochester
1-R21-EB026665-01A1 Developing Database and Software infrastructure for Quantitative Radiologic Analysis of Lumbar Radiculopathy Luke Macyszyn University of California Los Angeles
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