A robust deep learning framework for automated fetal behavioral state classification: leveraging multi-center datasets to improve antepartum heart rate monitoring [0.03%]
一种稳健的深度学习框架，用于自动化的胎儿行为状态分类：利用多中心数据集改进产前心率监测
Giulio Steyde,Isabelle Mueller,Margaret C Shair et al.
Giulio Steyde et al.
Fetal behavioral states reflect the developing brain's capacity to organize behavior into distinct sleep state patterns and are a window to evaluate the maturation of the nervous system throughout gestation. This study presents a deep learn...
Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging [0.03%]
自监督深度学习在超声微血管成像去噪中的应用研究
Lijie Huang,Jingyi Yin,Jingke Zhang et al.
Lijie Huang et al.
Ultrasound microvascular imaging (UMI) has emerged as a powerful, noninvasive modality for visualizing the microvasculature, offering valuable insights into underlying pathological and physiological processes. However, it is often hindered ...
PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT [0.03%]
基于变换器的域适应方法在双中心定量计算机断层扫描股骨近端分割中的应用(PF-DAformer)
Rochak Dhakal,Chen Zhao,Zixin Shi et al.
Rochak Dhakal et al.
Quantitative computed tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proximal femur. However, deploying automated segmentation models in...
Reconstructing 12-lead ECG from reduced lead sets using an encoder-decoder convolutional neural network [0.03%]
基于编码器解码器卷积神经网络的简化导联心电图重建研究
Dorsa EPMoghaddam,Anton Banta,Allison Post et al.
Dorsa EPMoghaddam et al.
The standard 12-lead electrocardiogram (ECG) is the gold-standard clinical tool for assessing the heart's electrical activity. The primary goal of this study is to reduce the number of recording sites needed to capture the same amount of in...
Explainable artificial intelligence in electrocardiography: A systematic review [0.03%]
心电图的可解释人工智能：系统性综述
Amirsajjad Taleban,Rodney Sparapani,Patrick Noffke et al.
Amirsajjad Taleban et al.
Electrocardiography (ECG) is a cornerstone of cardiac diagnostics, detecting cardiac pathologies ranging from arrhythmias to myocardial infarction. To enhance diagnostic accuracy and efficiency, deep learning models have been developed that...
SeRL: Style-embedding representation learning for unsupervised CT images synthesis from unpaired MR images [0.03%]
基于不成对的MR图像的无监督CT图像合成的风格嵌入表示学习-SeRL
Lei You,Hongyu Wang,Eduardo J Matta et al.
Lei You et al.
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, and the fastest-growing cause of cancer deaths in the United States. Computed tomography (CT) and magnetic resonance (MR) imaging are the key imag...
CNN-Autoformer: Automated EEG-Based Seizure Detection and Localization Using Hybrid Deep Learning [0.03%]
基于EEG的自动癫痫检测和定位的混合深度学习方法(CNN-Autoformer)
Shuhao Ren,Haotian Li,Weisen Lu et al.
Shuhao Ren et al.
Epilepsy is a neurological disorder characterized by transient and recurrent abnormal brain activity, often diagnosed through manual inspection extensive analysis of electroencephalogram (EEG) recordings. However, existing deep-learning sei...
Generalizable Multimodal Retinal Image Registration via Label-free Vessel Segmentation [0.03%]
无标签血管分割的可推广多模态视网膜图像配准方法
Utkarsh Doshi,Elli Davis,Mayss Al-Sheikh et al.
Utkarsh Doshi et al.
Multimodal retinal imaging plays a crucial role in diagnosing and managing various retinal diseases such as diabetic retinopathy and age-related macular degeneration (AMD). The majority of retinal imaging modalities including Color Fundus P...
Explainable Automated Seizure Detection using Attentive Deep Multi-View Networks [0.03%]
基于多视角注意深度网络的可解释性癫痫自动检测方法
Aref Einizade,Samaneh Nasiri,Mohsen Mozafari et al.
Aref Einizade et al.
Manual inspection of Electroencephalography (EEG) signals to detect epileptic seizures is time-consuming and prone to inter-rater variability. Moreover, EEG signals are contaminated with different noise sources, e.g., patient movement durin...
Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals [0.03%]
基于领域适应的声门下压力估计的发音人相关建模
Emiro J Ibarra,Julián D Arias-Londoño,Juan I Godino-Llorente et al.
Emiro J Ibarra et al.
Subglottal air pressure is a critical physiologically-based parameter that reveals fundamental pathophysiological processes in patients with voice disorders. However, its assessment in both laboratory and ambulatory settings presents signif...