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
Related News
With their eclectic mix of mutations, tumors often survive drug treatment. In a new study, researchers found a way to use cancer’s evolutionary potential against it, destroying drug-resistant tumors in animals.
A team of researchers from Johns Hopkins University recently investigated how skin tone affects the visibility of breast cancer targets in photoacoustic imaging. They found that that a new imaging technique reduces skin tone bias, improving visibility across diverse skin tones. Source: The International Society for Optics and Photonics.
A team of researchers led by Rice University’s Jacob Robinson and the University of Texas Medical Branch’s Peter Kan with NIBIB funding have developed a technique for diagnosing, managing and treating neurological disorders with minimal surgical risks. Source: Rice University News.
A team of researchers at Vanderbilt University has developed a system of artificial cilia capable of monitoring mucus conditions in human airways to better detect infection, airway obstruction, or the severity of diseases like Cystic Fibrosis (CF), Chronic Obstructive Pulmonary Diseases (COPD) and lung cancer. The lead researcher was funded by an NIH/NIBIB Trailblazer Award. Source: Vanderbilt School of Engineering
NIBIB has designed an initiative called Enhancing Biomedical Engineering, Imaging, and Technology Acceleration (eBEITA) at HBCUs. Recently, NIBIB made its first round of eBEITA grants to two HBCUs.