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Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19

arXiv 2311.06258 ↗First posted 24 Sep 2023cs.CY cs.AIcited by 0

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Abstract (arXiv)

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.

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References · 16

  1. 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
  2. 2020Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learningcited by 67
  3. 2020JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentationcited by 52
  4. 2020Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detectioncited by 46
  5. 2020Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Imagescited by 43
  6. 2020COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Networkcited by 29
  7. 2020Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Imagescited by 26
  8. 2020An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosiscited by 22
  9. 2021COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachcited by 8
  10. 2023Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentationscited by 8
  11. 2022Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labelscited by 5
  12. 2022CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Interventioncited by 5
4 more
  1. 2021Few-shot Learning for CT Scan based COVID-19 Diagnosiscited by 4
  2. 2021Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Datacited by 4
  3. 2021COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DBcited by 3
  4. 2023CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19cited by 2

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