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Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification
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DOI：10.1109/ACCESS.2021.3125324.png)
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摘要
En 中文
Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are frequently used in the diagnosis of respiratory diseases such as pneumonia or COVID-19. In this paper, we propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. Pretraining is achieved through the contrastive learning approach by comparing representations of differently augmented input images. The learned representations are transferred to downstream tasks - the classification of respiratory diseases. We evaluate the proposed approach on two tasks for pneumonia classification, one for COVID-19 recognition and one for discrimination of different pneumonia types. The results show that our approach yields competitive results without requiring large amounts of labeled training data.
Keyword:
COVID-19
Pulmonary diseases
Convolutional neural networks
Task analysis
X-ray imaging
Lung
Feature extraction
Self-supervised learning
contrastive learning
deep learning
convolutional neural network
chest X-ray
COVID-19
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
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