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.

翻译：我们的激励应用是一个现实世界的问题:CT成像中的COVID-19分类(COVID-19),为此,我们提出了一个基于半监督的“深学习”方法,该方法以半监督的分类管道为基础,使用可变自动编码器来提取高效的嵌入特性。我们优化了两个不同的CT图像网络的结构:(一) 一个新的有条件的有条件变异自动编码器(CVAE),其具体结构将编码器层内的类标签整合在一起,并使用共同注意的编码器侧边层信息,使代表学习最能利用背景线索;(二) 利用CVAE的编码器结构进行监督分类的下游脉动神经网络。根据可解释的分类结果,拟议的诊断系统对COVID-19分类非常有效。根据从质量和数量上获得的有希望的结果,我们设想在大规模临床研究中广泛运用我们开发的技术。Code可在https://git.etrovub.be/AVSP/ct-brovid-19-dianotototo.git上查阅。