DGEAHorNet: high-order spatial interaction network with dual cross global efficient attention for medical image segmentation [0.03%]
基于双交叉全局高效注意力的高阶空间交互网络医学图像分割
Haixin Peng,Xinjun An,Xue Chen et al.
Haixin Peng et al.
Medical image segmentation is a complex and challenging task, which aims to accurately segment various structures or abnormal regions in medical images. However, obtaining accurate segmentation results is difficult because of the great unce...
An approach for cancer outcomes modelling using a comprehensive synthetic dataset [0.03%]
利用全面合成数据集进行癌症预后建模的方法
Lorna Tu,Hervé H F Choi,Haley Clark et al.
Lorna Tu et al.
Limited patient data availability presents a challenge for efficient machine learning (ML) model development. Recent studies have proposed methods to generate synthetic medical images but lack the corresponding prognostic information requir...
Advanced bioimpedance analysis for infectious disease risk assessment via neural network classifiers [0.03%]
基于神经网络分类器的感染性疾病风险评估的高级生物阻抗分析
Sergey Filist,Riad Taha Al-Kasasbeh,Tigran Gagikovich Gevorkyan et al.
Sergey Filist et al.
In this work, a neural network classification model based on multidimensional bioimpedance measurement to analyze biomaterial impedance in living systems was developed. The modified Voigt model was used to capture the structural elements as...
Impact of differences in computed tomography value-electron density/physical density conversion tables on calculate dose in low-density areas [0.03%]
不同类型CT值-电子密度/物理密度转换表对低密度区域剂量计算的影响
Mia Nomura,Shunsuke Goto,Mizuki Yoshioka et al.
Mia Nomura et al.
In radiotherapy treatment planning, the extrapolation of computed tomography (CT) values for low-density areas without known materials may differ between CT scanners, resulting in different calculated doses. We evaluated the differences in ...
Evaluation of a digital bismuth germanium oxide PET/CT system according to the Japanese brain tumor phantom test for 18F-fluciclovine imaging [0.03%]
根据日本脑肿瘤体模测试评估数字锗酸铋PET/CT系统进行18F-氟氯酮成像的效果
Shohei Fukai,Hiromitsu Daisaki,Honoka Yoshida et al.
Shohei Fukai et al.
The Omni Legend (GE Healthcare), equipped with a digital bismuth germanium oxide PET/CT system, has been recently developed. However, the performance of the Omni Legend without a time-of-flight (TOF) system for 18F-fluciclovine imaging is s...
Optimizing LM-DRAMA parameters and non-local means filtering to improve small-lesion detectability in SiPM-based TOF breast PET [0.03%]
优化基于SiPM的TOF乳腺PET中小病灶检测能力的LM-DRAMA参数和非局部均值滤波器
Takuro Shiiba,Hana Katakami,Aiko Naito et al.
Takuro Shiiba et al.
This study aimed to optimize image reconstruction parameters for a dedicated time-of-flight (TOF) breast positron emission tomography (PET) system equipped with silicon photomultipliers (SiPMs) that maximize lesion detectability while minim...
Radiation exposure of young patients with abdominal neuroblastoma from therapeutic and imaging procedures: a phantom study [0.03%]
年轻腹部神经母细胞瘤患者治疗及影像检查中的辐射暴露：仿真人研究
Michalis Mazonakis,John Stratakis,Efrossyni Lyraraki et al.
Michalis Mazonakis et al.
This study calculated the radiation dose to young patients with high-risk abdominal neuroblastoma from therapeutic and imaging procedures. Computational XCAT phantoms representing typical patients aged 5-15 years were used. Intensity modula...
Hybrid deep-CNN and Bi-LSTM model with attention mechanism for enhanced ECG-based heart disease diagnosis [0.03%]
基于注意力机制的混合深度CNN和Bi-LSTM模型在ECG心病诊断中的应用研究
Gaurav Kumar,Neeraj Varshney
Gaurav Kumar
According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches ...
Comment on "Subclinical tremor differentiation using long short-term memory networks" [0.03%]
对“使用长短期记忆网络区分亚临床震颤”的评论
Hinpetch Daungsupawong,Viroj Wiwanitkit
Hinpetch Daungsupawong
A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals [0.03%]
基于ERP的多通道脑电信号分类的分布式自适应网络框架
Fatemeh Afkhaminia,Mohammad Bagher Shamsollahi,Tahereh Bahraini
Fatemeh Afkhaminia
Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffus...