Allows users to classify red blood cells (RBCs). This program is a high-throughput sickle cell classification method based on the deep Convolutional Neural Networks (dCNNs). It can be used in clinical test for: (1) assessing patient’s disease severity via longitudinal tracking and patient-specific RBC mapping; and (2) intervention strategies via personalized medicine treatment monitoring.
Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China; Division of Applied Mathematics, Brown University, Providence, RI, USA; Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Applied Mathematics, Brown University, Providence, RI, USA
MengjiaEtAl2017 funding source(s)
Supported by the National Institutes of Health (NIH) grant U01HL114476 and China Scholarship Council; and the Singapore-MIT Alliance for Research and Technology (SMART) Center and NIH grant R01HL121386.