| 5-R01-EB033788-02 |
Maternal mHealth blood hemoglobin analysis with informed deep learning |
Young Kim |
Purdue University |
| 5-R01-EB029944-04 |
MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants |
Lili He |
Cincinnati Childrens Hosp Med Ctr |
| 1-K25-EB035166-01 |
New Tools for Enhancing Cerebral Angiography: From Planning to Navigation |
Nazim Haouchine |
Brigham And Women'S Hospital |
| 5-R01-EB031032-04 |
Non-invasive automated wound analysis via deep learning neural networks |
Kyle Quinn |
University of Arkansas at Fayetteville |
| 1-R44-EB030955-01A1 |
Opening The Black Box: Enhancing Machine Learning Interpretability To Optimize Clinical Response To Sudden Deterioration In COVID-19 Patients |
Dana Edelson |
Agilemd, Inc. |
| 1-R01-EB035394-01A1 |
Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care Unit |
Dufan Wu |
Massachusetts General Hospital |
| 5-R01-EB022573-08 |
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth |
Yong Fan |
University of Pennsylvania |
| 5-R01-EB030582-04 |
Quantification of Liver Fibrosis with MRI and Deep Learning |
Lili He |
Cincinnati Childrens Hosp Med Ctr |
| 5-R01-EB021391-08 |
Shape Analysis Toolbox: From medical images to quantitative insights of anatomy |
Beatriz Paniagua |
Kitware, Inc. |
| 5-R01-EB001838-16 |
Simulation Tools for 3D and 4D CT and Dosimetry |
William Segars |
Duke University |
