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Related Experiment Video

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Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.

Zhongliang Li1, Xuechen Li2, Zhihao Jin1

  • 1AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 Guangdong China.

Neural Computing & Applications
|May 8, 2023
PubMed
Summary

This study introduces a new self-supervised learning method for diagnosing COVID-19 using thoracic CT scans. The approach effectively trains models without labeled data, improving diagnostic accuracy for COVID-19 detection.

Keywords:
COVID-19 diagnosisLesion modelingSelf-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Thoracic computed tomography (CT) is crucial for diagnosing Coronavirus disease 2019 (COVID-19).
  • Deep learning models excel in image recognition but typically require extensive annotated data for training.
  • A significant challenge in COVID-19 diagnosis using CT is the need for large, labeled datasets.

Purpose of the Study:

  • To develop a novel self-supervised pretraining method for COVID-19 diagnosis using CT scans.
  • To overcome the limitation of requiring large annotated datasets in deep learning for medical image analysis.
  • To enhance the feature representation capabilities of models for accurate COVID-19 detection.

Main Methods:

  • A self-supervised pretraining strategy was proposed, utilizing pseudo-lesion generation and restoration.
  • Perlin noise was employed to create artificial lesion-like patterns, which were integrated into normal CT images to form pseudo-COVID-19 images.
  • An encoder-decoder U-Net architecture was trained on pairs of normal and pseudo-COVID-19 images for image restoration, followed by fine-tuning the encoder for the diagnosis task.

Main Results:

  • The self-supervised learning approach demonstrated superior feature extraction for COVID-19 diagnosis compared to traditional methods.
  • The proposed method achieved higher accuracy, outperforming a supervised model pretrained on large-scale images by 6.57% on the SARS-CoV-2 dataset.
  • Accuracy improvements of 3.03% were observed on the Jinan COVID-19 dataset, validating the method's effectiveness.

Conclusions:

  • The proposed self-supervised learning method offers a viable solution for COVID-19 diagnosis using CT scans, particularly when labeled data is scarce.
  • This approach effectively leverages unlabeled data to improve deep learning model performance in medical image analysis.
  • The study highlights the potential of generative techniques and self-supervised learning to advance automated disease diagnosis.