论文标题
可解释的逐日设计半监督的代表性学习，用于从CT成像中诊断的COVID诊断
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
论文作者
论文摘要
我们激励的应用是一个现实世界中的问题：CT成像中的COVID-19分类，为此，我们基于一种可解释的深度学习方法，基于半监督分类管道，该方法采用了变异自动编码器来提取有效的功能嵌入。 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.有了可解释的分类结果，提出的诊断系统对于COVID-19分类非常有效。基于定性和定量获得的有希望的结果，我们设想在大规模临床研究中广泛部署我们开发的技术。代码可在https://git.etrovub.be/avsp/ct-covs-covid-covid-covid-19-diarostic-tool.git上获得。
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.
