COVID-index: A texture-based approach to classifying lung lesions based on CT images [0.03%]
基于CT图像的纹理方法在肺炎病变分类中的应用
Vitória de Carvalho Brito,Patrick Ryan Sales Dos Santos,Nonato Rodrigues de Sales Carvalho et al.
Vitória de Carvalho Brito et al.
COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As ...
COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network [0.03%]
基于多核大小空间通道注意力网络的COVID-19 X射线图像检测方法
Yuqi Fan,Jiahao Liu,Ruixuan Yao et al.
Yuqi Fan et al.
Novel coronavirus 2019 (COVID-19) has spread rapidly around the world and is threatening the health and lives of people worldwide. Early detection of COVID-19 positive patients and timely isolation of the patients are essential to prevent i...
Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images [0.03%]
基于胸部CT图像的放射组学特征的肺部分割和COVID-19自动检测技术
Chen Zhao,Yan Xu,Zhuo He et al.
Chen Zhao et al.
This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation ...
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies [0.03%]
基于新型冠状病毒肺炎检测研究的医学图像深度学习模型责任清单
Weronika Hryniewska,Przemysław Bombiński,Patryk Szatkowski et al.
Weronika Hryniewska et al.
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modu...
Joint segmentation and detection of COVID-19 via a sequential region generation network [0.03%]
基于序列区域生成网络的COVID-19联合分割与检测方法
Jipeng Wu,Shengchuan Zhang,Xi Li et al.
Jipeng Wu et al.
The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) di...
Periphery-aware COVID-19 diagnosis with contrastive representation enhancement [0.03%]
基于对比表示增强的周边感知COVID-19诊断方法
Junlin Hou,Jilan Xu,Longquan Jiang et al.
Junlin Hou et al.
Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumoni...
Detection of COVID-19 from speech signal using bio-inspired based cepstral features [0.03%]
基于生物启发的倒谱特征的语音信号新冠病毒检测方法
Tusar Kanti Dash,Soumya Mishra,Ganapati Panda et al.
Tusar Kanti Dash et al.
The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easil...
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images [0.03%]
用于少量样本肺部CT图像新冠肺炎诊断的动量对比学习方法
Xiaocong Chen,Lina Yao,Tao Zhou et al.
Xiaocong Chen et al.
The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Contro...
Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19 [0.03%]
用于自动CT和X光诊断COVID-19的多任务对比学习方法
Jinpeng Li,Gangming Zhao,Yaling Tao et al.
Jinpeng Li et al.
Computed tomography (CT) and X-ray are effective methods for diagnosing COVID-19. Although several studies have demonstrated the potential of deep learning in the automatic diagnosis of COVID-19 using CT and X-ray, the generalization on uns...
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images [0.03%]
基于CT图像的新型冠状病毒肺炎自动严重程度评估的肺叶分割和层次多实例分类协同学习方法
Kelei He,Wei Zhao,Xingzhi Xie et al.
Kelei He et al.
Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in id...