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Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

arXiv 2004.02731 ↗First posted 6 Apr 2020eess.IV cs.CV q-bio.QMcited by 62

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Abstract (arXiv)

(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2020.

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Citing papers per year: 2021: 35; 2022: 15; 2023: 10; 2024: 2.

62 papers.

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  1. 2021Concealed Object Detectioncited by 212
  2. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
  3. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  4. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  5. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
  6. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  7. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  8. 2021TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Imagescited by 12
  9. 2024Robust COVID-19 Detection in CT Images with CLIPcited by 12
  10. 2024Multi-Dataset Multi-Task Learning for COVID-19 Prognosiscited by 11
  11. 2021COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachcited by 8
  12. 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
38 more
  1. 2021An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scanscited by 7
  2. 2021Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learningcited by 7
  3. 2021COVID-19 Modeling: A Reviewcited by 7
  4. 2022Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classificationcited by 7
  5. 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
  6. 2021Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentationcited by 5
  7. 2021Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Imagescited by 5
  8. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
  9. 2022Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labelscited by 5
  10. 2023MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attentioncited by 5
  11. 2021CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scanscited by 4
  12. 2021Domain adaptation based self-correction model for COVID-19 infection segmentation in CT imagescited by 4
  13. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  14. 2022BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Datacited by 4
  15. 2022Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19cited by 4
  16. 2021Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagescited by 3
  17. 2021Automated lung segmentation from CT images of normal and COVID-19 pneumonia patientscited by 3
  18. 2021COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approachcited by 3
  19. 2022Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Imagescited by 3
  20. 2023DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problemcited by 3
  21. 2023Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentationcited by 3
  22. 2021Machine learning applications for COVID-19: A state-of-the-art reviewcited by 2
  23. 2021Semi-supervised Learning for COVID-19 Image Classification via ResNetcited by 2
  24. 2021Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentationcited by 2
  25. 2021CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Imagescited by 2
  26. 2022Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentationcited by 2
  27. 2023MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomographycited by 2
  28. 2021An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Diseasecited by 1
  29. 2021Can Self Reported Symptoms Predict Daily COVID-19 Cases?cited by 1
  30. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  31. 2021Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the Systemcited by 1
  32. 2022Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistencycited by 1
  33. 2022Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Netcited by 1
  34. 2022COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infectioncited by 1
  35. 2023Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Searchcited by 1
  36. 2023Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Imagescited by 1
  37. 2023Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnosticscited by 1
  38. 2023Density Matters: Improved Core-set for Active Domain Adaptive Segmentationcited by 1

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