mlsanityData to 14 Oct 2026

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images

arXiv 2112.04984 ↗First posted 9 Dec 2021eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 9 Dec 2021.

Builds on

26 of 62 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  6. 2014Going Deeper with Convolutionscited by 4,241
  7. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  8. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
  9. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  10. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  11. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 201
  12. 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
14 more
  1. 2019Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentationcited by 121
  2. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  3. 2020Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptationcited by 81
  4. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 76
  5. 2020Deep Learning COVID-19 Features on CXR using Limited Training Data Setscited by 68
  6. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
  7. 2014LSDA: Large Scale Detection Through Adaptationcited by 32
  8. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  9. 2020Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Imagescited by 26
  10. 2020Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumoniacited by 26
  11. 2021Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Studycited by 11
  12. 2018Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Imagescited by 10
  13. 2014Detector Discovery in the Wild: Joint Multiple Instance and Representation Learningcited by 6
  14. 2021Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Datacited by 6

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 2.

5 papers.

  1. 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 35
  2. 20223D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomographycited by 2
  3. 2024INSIGHT: Explainable Weakly-Supervised Medical Image Analysiscited by 1
  4. 2022Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challengescited by 0
  5. 2024Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generationcited by 0