伪病变学习:COVID-19诊断的自监督框架

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhongliang Li, Xuechen Li, Zhihao Jin, Linlin Shen
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引用次数: 0

摘要

2019冠状病毒病(COVID-19)自2019年12月首次报告以来,在全球迅速传播,胸部计算机断层扫描(CT)已成为其诊断的主要工具之一。近年来,基于深度学习的方法在无数图像识别任务中表现出令人印象深刻的性能。然而,它们通常需要大量带注释的数据进行训练。受COVID-19患者CT扫描中常见的磨玻璃不透明现象的启发,我们提出了一种基于伪病变生成和恢复的自监督预训练方法用于COVID-19诊断。我们使用基于梯度噪声的数学模型Perlin噪声生成病变样模式,然后将其随机粘贴到正常CT图像的肺部区域以生成伪covid -19图像。然后使用正常和伪covid -19图像对来训练基于编码器-解码器架构的U-Net用于图像恢复,该方法不需要任何标记数据。然后使用标记数据对预训练的编码器进行微调,用于COVID-19诊断任务。采用由CT图像组成的两个公开的COVID-19诊断数据集进行评估。综合实验结果表明,本文提出的自监督学习方法可以更好地提取COVID-19诊断的特征表示,在SARS-CoV-2数据集和济南COVID-19数据集上,该方法的准确率分别比大规模图像上预训练的监督模型高6.57%和3.03%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019, and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by ground glass opacity, a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo-lesion generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo-COVID-19 images. The pairs of normal and pseudo-COVID-19 images were then used to train an encoder-decoder architecture-based U-Net for image restoration, which does not require any labeled data. The pretrained encoder was then fine-tuned using labeled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis, and the accuracy of the proposed method outperformed the supervised model pretrained on large-scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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