With the massive damage in the world caused by Coronavirus Disease 2019
SARS-CoV-2 (COVID-19), many related research topics have been proposed in the
past two years. The Chest Computed Tomography (CT) scans are the most valuable
materials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19
classification of Chest CT scan is based on a single-slice level, implying that
the most critical CT slice should be selected from the original CT scan volume
manually. We simultaneously propose 2-D and 3-D models to predict the COVID-19
of CT scan to tickle this issue. In our 2-D model, we introduce the Deep
Wilcoxon signed-rank test (DWCC) to determine the importance of each slice of a
CT scan to overcome the issue mentioned previously. Furthermore, a
Convolutional CT scan-Aware Transformer (CCAT) is proposed to discover the
context of the slices fully. The frame-level feature is extracted from each CT
slice based on any backbone network and followed by feeding the features to our
within-slice-Transformer (WST) to discover the context information in the pixel
dimension. The proposed Between-Slice-Transformer (BST) is used to aggregate
the extracted spatial-context features of every CT slice. A simple classifier
is then used to judge whether the Spatio-temporal features are COVID-19 or
non-COVID-19. The extensive experiments demonstrated that the proposed CCAT and
DWCC significantly outperform the state-of-the-art methods.

该研究提出了一种同时使用 2-D 和 3-D 模型的方法，通过 Deep Wilcoxon signed-rank test （DWCC）来确定 Chest CT scan 的每一层的重要性，并借助 Convolutional CT scan-Aware Transformer (CCAT) 发现 Chest CT scan 的上下文信息，以判断出其是否为 COVID-19，该方法较之现有方法具有更好的性能。

COVID-19 分类的统计测试视觉变换器

Visual Transformer with Statistical Test for COVID-19 Classification

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist
medical specialists in vital circumstances. Deep learning methodologies
constitute a main approach for chest CT scan analysis and disease prediction.
However, large annotated databases are necessary for developing deep learning
models that are able to provide COVID-19 diagnosis across various medical
environments in different countries. Due to privacy issues, publicly available
COVID-19 CT datasets are highly difficult to obtain, which hinders the research
and development of AI-enabled diagnosis methods of COVID-19 based on CT scans.
In this paper we present the COV19-CT-DB database which is annotated for
COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in
training, validation and test datasets. The former two datasets can be used for
training and validation of machine learning models, while the latter will be
used for evaluation of the developed models. We also present a deep learning
approach, based on a CNN-RNN network and report its performance on the
COVID19-CT-DB database.

本文介绍了一个包含约 5,000 个三维 CT 扫描的标注 COVID-19 的数据库（COV19-CT-DB），其中分为训练集、验证集和测试集，并且提供了基于 CNN-RNN 网络的深度学习方法以及其在该数据库上的性能报告。

MIA-COV19D: 基于三维胸部 CT 图像分析的 COVID-19 检测

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic
disease in over 200 countries, influencing billions of humans. To control the
infection, identifying and separating the infected people is the most crucial
step. The main diagnostic tool is the Reverse Transcription Polymerase Chain
Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high
enough to effectively prevent the pandemic. The chest CT scan test provides a
valuable complementary tool to the RT-PCR test, and it can identify the
patients in the early-stage with high sensitivity. However, the chest CT scan
test is usually time-consuming, requiring about 21.5 minutes per case. This
paper develops a novel Joint Classification and Segmentation (JCS) system to
perform real-time and explainable COVID-19 chest CT diagnosis. To train our JCS
system, we construct a large scale COVID-19 Classification and Segmentation
(COVID-CS) dataset, with 144,167 chest CT images of 400 COVID-19 patients and
350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with
fine-grained pixel-level labels of opacifications, which are increased
attenuation of the lung parenchyma. We also have annotated lesion counts,
opacification areas, and locations and thus benefit various diagnosis aspects.
Extensive experiments demonstrate that the proposed JCS diagnosis system is
very efficient for COVID-19 classification and segmentation. It obtains an
average sensitivity of 95.0% and a specificity of 93.0% on the classification
test set, and 78.5% Dice score on the segmentation test set of our COVID-CS
dataset. The COVID-CS dataset and code are available at
this https URL

该论文开发了一种新型联合分类和分割（JCS）系统，用于实时和可解释的冠状病毒肺部 CT 诊断，并通过 COVID-CS 数据集的实验表明，该系统在 COVID-19 分类和分割方面是非常有效的。