Deep Learning Applications for COVID-19
Jan 19, 2021
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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.
Authors: Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht (Florida Atlantic University)
Paper Links Mentioned in the Video:
Mapping the Landscape of Artificial Intelligence Applications against COVID-19: https://arxiv.org/abs/2003.11336
Leveraging Data Science to Combat COVID-19: A Comprehensive Review: https://www.techrxiv.org/articles/preprint/Leveraging_Data_Science_To_Combat_COVID-19_A_Comprehensive_Review/12212516
AlphaFold2: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology
Molecular Representation Learning with Language Models and Auxiliary Tasks: https://arxiv.org/abs/2011.13230
Data Professor - Machine Learning for Drug Discovery explained in 2 minutes: https://www.youtube.com/watch?v=xDMzOUUnNzw
Understanding LSTM Networks - Christopher Olah: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Vision Transformer: https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html
Thomas Kipf - Graph Convolutional Networks: https://tkipf.github.io/graph-convolutional-networks/
Attention is all you Need: https://arxiv.org/abs/1706.03762
GLUE Benchmark: https://gluebenchmark.com/
SQuAD Dataset Page: https://rajpurkar.github.io/SQuAD-explorer/
TL;DR Nature Article: https://www.nature.com/articles/d41586-020-03277-2
TREC-COVID: https://arxiv.org/abs/2005.04474
CO-Search: https://arxiv.org/abs/2006.09595
BenevolentAI: https://www.benevolent.com/
SciFACT Demo App: https://scifact.apps.allenai.org/
FAIR / NYU COVID-19 CXR Deterioration Modeling: https://ai.facebook.com/blog/new-ai-research-to-help-predict-covid-19-resource-needs-from-a-series-of-x-rays/
MoCo pretraining in CXR: https://arxiv.org/abs/2010.05352
OpenAI CLIP: https://openai.com/blog/clip/
Contrastive Learning of Medical Visual Representations from Paired Images and Text: https://arxiv.org/abs/2010.00747
Illuminating the dark spaces of healthcare with Ambient Intelligence: https://www.nature.com/articles/s41586-020-2669-y
CovariantAI: https://covariant.ai/
Solving Rubik's Cube with a Robot Hand: https://openai.com/blog/solving-rubiks-cube/
Berkeley AI Research Blog: https://bair.berkeley.edu/blog/
Four Novel Approaches to Manipulate Fabric: https://bair.berkeley.edu/blog/2020/05/05/fabrics/
Ingredients of Real-World Robotic Learning: https://bair.berkeley.edu/blog/2020/04/27/ingredients/
RoboNet: https://bair.berkeley.edu/blog/2019/11/26/robo-net/
Plan2Explore: https://bair.berkeley.edu/blog/2020/10/06/plan2explore/
An Empirical Study of Representation Learning for RL in Healthcare: https://arxiv.org/abs/2011.11235
AlphaFold for COVID Proteins: https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19
Artificial Intelligence in COVID-19 drug repurposing: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30192-8/fulltext
Worldometeres COVID Cases: https://www.worldometers.info/coronavirus/
Hi-COVIDNet: https://dl.acm.org/doi/10.1145/3394486.3412864
LIME: https://arxiv.org/abs/1602.04938
DeepDream: https://en.wikipedia.org/wiki/DeepDream
WT5?! https://arxiv.org/abs/2004.14546
CheckList: https://arxiv.org/abs/2005.04118
DALL-E: https://openai.com/blog/dall-e/
On the steerability of GANs: https://arxiv.org/abs/1907.07171
Federated Learning: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Andrew Trask on Privacy Preserving AI: https://www.youtube.com/watch?v=4zrU54VIK6k&t=870s
On the Measure of Intelligence: https://arxiv.org/abs/1911.01547
0:00 Intro 1:05 Video Outline 2:11 Application Preview 6:55 Limitations Preview 8:00 Data Science for COVID 9:30 Learning Tasks 18:00 Input Data for Deep Learning 34:06 Natural Language Processing 54:43 Computer Vision 1:07:35 Life Sciences 1:18:18 Epidemiology 1:22:28 Limitations of Deep Learning 1:36:23 On the Measure of Intelligence 1:37:14 Takeaways and Lessons Learned
0:00 Intro 1:05 Video Outline 2:11 Application Preview 6:55 Limitations Preview 8:00 Data Science for COVID 9:30 Learning Tasks 18:00 Input Data for Deep Learning 34:06 Natural Language Processing 54:43 Computer Vision 1:07:35 Life Sciences 1:18:18 Epidemiology 1:22:28 Limitations of Deep Learning 1:36:23 On the Measure of Intelligence 1:37:14 Takeaways and Lessons Learned
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