Corona-Net

Fighting COVID-19 with Machine Learning

Ching Lam Choi

Computer Vision Data Science Deep Learning Image Processing Scientific Libraries (Numpy/Pandas/SciKit/...)

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Identified in December 2019, the novel Coronavirus has infected 2.7 million worldwide, and claimed the lives of 0.2 million. Amidst this deadly pandemic, I started my open source project, Corona-Net, in the hopes of contributing to the global fight against the Coronavirus. Corona-Net is a 3-part project dedicated to the classification, binary segmentation and multi-class segmentation of COVID-19. I first leverage the EfficientNet model for COVID-19 diagnosis, then utilise and refine the U-Net architecture for both binary and 3-class (ground-glass, consolidation, pleural effusion) segmentation of COVID-19 symptoms, through inference on the COVID-19 CT segmentation (chest axial CT) dataset. Through Corona-Net, I aim to develop a reliable, visual-semantically balanced method for automatic COVID-19 diagnosis, as well as extend an invitation to all to collaborate and stand together against this pandemic. My PyTorch code is publicly available at https://github.com/chinglamchoi/Corona-Net.

Type: Talk (30 mins); Python level: Intermediate; Domain level: Intermediate


Ching Lam Choi

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.