Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records [0.03%]
墨西哥医疗工作者队列中新冠肺炎的预警：智能手表生理记录离散贝塔分布熵分析
Alejandro Aguado-García,América Arroyo-Valerio,Galileo Escobedo et al.
Alejandro Aguado-García et al.
We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 conti...
ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images [0.03%]
基于注意力密集U型网络的深度监督神经网络：自动分割新冠患者胸部CT图像中的病灶区域
Sanjib Saha,Subhadeep Dutta,Biswarup Goswami et al.
Sanjib Saha et al.
An automatic method for qualitative and quantitative evaluation of chest Computed Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop an automated COVID-19 prediction framework using deep learning. We put...
Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images [0.03%]
基于全特征融合的神经网络在CT图像中对新冠肺炎病灶分割中的应用研究
Wei Li,Yangyong Cao,Shanshan Wang et al.
Wei Li et al.
Coronavirus Disease 2019 (COVID-19) spreads around the world, seriously affecting people's health. Computed tomography (CT) images contain rich semantic information as an auxiliary diagnosis method. However, the automatic segmentation of CO...
SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images [0.03%]
超轻量级的基于CT图像的新型冠状病毒肺炎病灶分割网络：SuperMini-seg
Yuan Yang,Lin Zhang,Lei Ren et al.
Yuan Yang et al.
The automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images is helpful in establishing a quantitative model to diagnose and treat COVID-19. To this end, this study proposes a lightweight segmentation network cal...
Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation [0.03%]
基于概率活动轮廓和CNN分割的半监督CT图像COVID-19体积肺病变估计
Diomar Enrique Rodriguez-Obregon,Aldo Rodrigo Mejia-Rodriguez,Leopoldo Cendejas-Zaragoza et al.
Diomar Enrique Rodriguez-Obregon et al.
Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. ...
Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning [0.03%]
一种改进的渐进学习多头深度学习框架用于肺部疾病检测及严重程度评分
Asad Mansoor Khan,Muhammad Usman Akram,Sajid Nazir et al.
Asad Mansoor Khan et al.
Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberc...
MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images [0.03%]
一种用于使用胸部X光片进行COVID-19分类的多纹理多类注意循环残差卷积神经网络方法
Anandbabu Gopatoti,P Vijayalakshmi
Anandbabu Gopatoti
Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to r...
A sensorless, physiologic feedback control strategy to increase vascular pulsatility for rotary blood pumps [0.03%]
一种无传感器生理反馈控制策略，用于增加旋转血液泵的血管脉动性
Zhehuan Tan,Mingming Huo,Kairong Qin et al.
Zhehuan Tan et al.
Continuous flow rotary blood pumps (RBP) operating clinically at constant rotational speeds cannot match cardiac demand during varying physical activities, are susceptible to suction, diminish vascular pulsatility, and have an increased ris...
Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models [0.03%]
基于机器学习模型的临床数据全面分析以估算COVID-19结局
Daniel I Morís,Joaquim de Moura,Pedro J Marcos et al.
Daniel I Morís et al.
COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. I...
Interpretation of lung disease classification with light attention connected module [0.03%]
带轻量注意连接模块的肺部疾病分类解释
Youngjin Choi,Hongchul Lee
Youngjin Choi
Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model c...