Explore an open-source project dedicated to combating COVID-19 through machine learning in this EuroPython 2020 conference talk. Dive into Corona-Net, a three-part initiative focusing on classification, binary segmentation, and multi-class segmentation of COVID-19 using chest CT scans. Learn about the implementation of EfficientNet for diagnosis and the refinement of U-Net architecture for symptom segmentation. Discover how this project aims to develop a reliable, visually-semantically balanced method for automatic COVID-19 diagnosis while inviting collaboration in the global fight against the pandemic. Gain insights into the project's background, problem statement, model architecture, classification techniques, fully convolutional networks, and future development plans.
Overview
Syllabus
Intro
Self Intro
Background
Problem Statement
Model Architecture
Classification
Fully Convolutional Networks
Future Development
Taught by
EuroPython Conference
