Fighting COVID-19 with Machine Learning

Fighting COVID-19 with Machine Learning

Fighting COVID-19 with Machine Learning (Ching Lam Choi) (Hong Kong) is an English session in the online PyCon HK 2020 Spring.

Fighting COVID-19 with Machine Learning (Ching Lam Choi) (Hong Kong)

In this talk, I will introduce my open source Python project, COVID-19 chest CT segmentation with PyTorch. I leverage the UNet model — a Fully Convolutional encoder-decoder network — for 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.

Level: Intermediate

Slide: https://github.com/pyconhk/pyconhk-presentations/blob/master/2020spring/Corona-Net.pdf

Speaker Bio: 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.

https://github.com/chinglamchoi
https://www.linkedin.com/in/ching-lam-7609541a0/
https://twitter.com/cchoi314
https://medium.com/@cchoi314

Session Time in HKT: 10:00 AM on 8 May 2020 Friday.
Session Time in GMT: 2:00 AM on 8 May 2020 Friday.