Abstract
This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.
Highlights
SARS-CoV-2 and the resulting COVID-19 disease is one of the biggest challenges of the 21st century
Deep Learning is advancing very quickly, but what is the current state of this technology? What problems does Deep Learning have the capability of solving? How do we articulate COVID-19 problems for the application of Deep Learning? We explore these questions through the lens of Deep Learning applications fighting COVID19 in many ways
We provide an exhaustive list of applications in data domains such as Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology
Summary
SARS-CoV-2 and the resulting COVID-19 disease is one of the biggest challenges of the 21st century. For each application area surveyed, we provide a detailed analysis of how the given data is inputted to a deep neural network and how learning tasks are constructed. Precision Medicine in COVID-19 applications looks at predicting patient outcome based on patient history recorded in Electronic Health Records (EHR), as well as miscellaneous biomarkers such as blood testing results This is another section that is highly relevant for our cautionary “Limitations of Deep Learning” with respect to Data Privacy. These models find the reproductive rate of the virus, which can characterize the danger of letting herd immunity develop naturally To formulate this as a Deep Learning task, a Deep Neural Network approximates the time-varying strength of quarantine in the SIR model, since integrating the Exposed population would require extremely detailed data. We take an optimistic look at these problems in our section “Limitations of Deep Learning”, explaining solutions to these problems as well, such as self-explanatory models or federated learning
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