Artificial Intelligence, Machine Learning, and Deep Learning

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