Quantitative Susceptibility Mapping MRI with Computer Vision Metrics to Reduce Scan Time for Brain Hemorrhage Assessment [0.03%]
用于缩短脑出血评估扫描时间的计算机视觉定量磁敏感性映射MRI技术
Huiyu Huang,Shreyas Balaji,Bulent Aslan et al.
Huiyu Huang et al.
Purpose: Optimizing clinical imaging parameters balances scan time and image quality. Quantitative Susceptibility Mapping (QSM) MRI, particularly for detecting intracranial hemorrhage (ICH), involves multiple echo times (...
Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T [0.03%]
基于压缩感知的3T氨基质子转移加权MRI的影像组学特征可靠性研究
Jingpu Wu,Qianqi Huang,Yiqing Shen et al.
Jingpu Wu et al.
Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer-weighted (APTw) images. Brain tumor MRI data of 40 sc...
Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics [0.03%]
基于多模态MRI影像组学的胶质母细胞瘤患者的无创预后生存时间预测
Jingyu Zhu,Jianming Ye,Leshui Dong et al.
Jingyu Zhu et al.
Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor is aggressive and shows high biological heterogeneity, the overall survival (OS) time is extremely low even with the most aggressive tre...
M Cheney,D Isaacson,J C Newell et al.
M Cheney et al.
The inverse conductivity problem is the mathematical problem that must be solved in order for electrical impedance tomography systems to be able to make images. Here we show how this inverse conductivity problem is related to a number of ot...
COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet [0.03%]
基于CAPA-ResUNet的胸部CT图像中新型冠状病毒肺炎病灶分割
Lu Ma,Shuni Song,Liting Guo et al.
Lu Ma et al.
Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve th...
Application of a novel T1 retrospective quantification using internal references (T1-REQUIRE) algorithm to derive quantitative T1 relaxation maps of the brain [0.03%]
一种新型T1回顾性量化使用内部参考(T1-REQUIRE)算法在大脑中获取定量T1弛豫图的应用研究
Adam Hasse,Julian Bertini,Sean Foxley et al.
Adam Hasse et al.
Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective asse...
A deep learning approach for classification of COVID and pneumonia using DenseNet-201 [0.03%]
基于DenseNet-201的COVID-19和肺炎分类深度学习方法
Harshal A Sanghvi,Riki H Patel,Ankur Agarwal et al.
Harshal A Sanghvi et al.
In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insuranc...
LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images [0.03%]
LiteCovidNet：一种使用X光图像检测COVID-19的轻量级深度神经网络模型
Sachin Kumar,Sourabh Shastri,Shilpa Mahajan et al.
Sachin Kumar et al.
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based de...
A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections [0.03%]
一种基于DeepLabV3+的COVID-19肺炎病灶分割算法研究
Hasan Polat
Hasan Polat
Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vita...
A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID-19 based on acoustic cough features [0.03%]
基于声学咳嗽特征的COVID-19动态诊断的深度神经网络架构比较分析
Gurram Sunitha,Rajesh Arunachalam,Mohammed Abd-Elnaby et al.
Gurram Sunitha et al.
The study aims to assess the detection performance of a rapid primary screening technique for COVID-19 that is purely based on the cough sound extracted from 2200 clinically validated samples using laboratory molecular testing (1100 COVID-1...