Artificial Intelligence, Machine Learning, and Deep Learning

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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

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