Day 2, R3 11:55–12:10
In this talk, I will introduce my open source Python project, Corona-Net, COVID-19 chest CT segmentation with PyTorch. I leverage the EfficientNet model for image-level COVID-19 diagnosis, as well as the UNet -- a Fully Convolutional encoder-decoder network -- for binary and multi-class medical segmentation. Through UNet, I successfully detect and localise COVID-19 symptoms, including ground-glass, consolidation and pleural effusion, from axial chest CT slices, using open source datasets and annotations.
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Speaker: Choi Ching Lam
Choi Ching Lam is a high school programmer from Hong Kong, keenly interested in Computer Vision and Scientific Computing. She is an open source enthusiast with a deep appreciation for Python and Julia. Presently an intern at NVIDIA's AI Tech Center, Ching Lam aspires to become a Machine Learning researcher.