This paper is responding to the MIA-COV19 challenge to classify COVID from
non-COVID based on CT lung images. The COVID-19 virus has devastated the world
in the last eighteen months by infecting more than 182 million people and
causing over 3.9 million deaths. The overarching aim is to predict the
diagnosis of the COVID-19 virus from chest radiographs, through the development
of explainable vision transformer deep learning techniques, leading to
population screening in a more rapid, accurate and transparent way. In this
competition, there are 5381 three-dimensional (3D) datasets in total, including
1552 for training, 374 for evaluation and 3455 for testing. While most of the
data volumes are in axial view, there are a number of subjects' data are in
coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D
data based classification is investigated, in this competition, 2D images
remains the main focus. Two deep learning methods are studied, which are vision
transformer (ViT) based on attention models and DenseNet that is built upon
conventional convolutional neural network (CNN). Initial evaluation results
based on validation datasets whereby the ground truth is known indicate that
ViT performs better than DenseNet with F1 scores being 0.76 and 0.72
respectively. Codes are available at GitHub at
<this https URL>.

本文通过建立可解释的视觉 Transformer 深度学习技术，旨在基于胸部 X 线片预测 COVID-19 肺炎的诊断结果，以期进行更快速、准确和规范的人群筛查，通过对病人的三维数据进行二分类的训练，分析和探究了两种深度学习方法，即建立在传统卷积神经网络（CNN）基础上的 DenseNet 和基于关注模型的视觉 Transformer，结果表明视觉 Transformer 表现更佳，F1 分数为 0.76。