Alissa is a data scientist at Imaging Research Center in the CCHMC. She is skilled in Python, C++, Cuda, Pytorch and Tensorflow. She is experienced in deep learning, computer vision, intersection between natural language processing and computer vision, medical image analysis.
Machine learning; Computer Vision; Deep learning; Mobile Development; Web Development
Radiology, Imaging Research Center
Master of Science (M.S.)
Electrical and Computer Engineering, Tufts University, Medford, MA
Bachelor of Science (B.S.)
Electrical Engineering, North China Electronic Power University, Beijing, China
Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks.
Introduction: Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. In this project, we developed a switchable CycleGAN model for image synthesis between multi-contrast brain MRI images using a large set of publicly accessible pediatric structural brain MRI images. We conducted extensive experiments to compare switchable CycleGAN with original CycleGAN both quantitatively and qualitatively. Experimental results demonstrate that switchable CycleGAN is able to outperform CycleGAN model on pediatric MRI brain image synthesis.