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    • Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

    Zenodo (CERN European Organization for Nuclear Research)（2022）

    Artificial Intelligence in Medicine (Canada)

    Cited 5|Views49
    Abstract
    Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git.
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    【要点】：该论文提出了一种基于CT影像的COVID-19半监督表征学习方法，该方法利用可解释的深度学习管道和变分自编码器提取高效特征嵌入，并通过优化两种不同的网络架构实现了CT图像的分类。
    

    【方法】：该方法采用了可解释的半监督分类管道，通过条件变分自编码器（CVAE）和下游卷积神经网络进行特征提取和分类。
    

    【实验】：实验使用了一个条件变分自编码器（CVAE）和一个下游卷积神经网络对COVID-19进行分类。CVAE将类别标签集成在编码器层中，并使用共享注意力层的侧信息进行编码，从而充分利用上下文线索进行表征学习。实验结果表明，该方法在COVID-19分类方面非常有效，其在定性和定量评估中均表现出良好的性能，有望在大型临床研究中得到广泛应用。相关代码可在 https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git 获取。
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