Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium
C Zhao, S Xiang, Y Wang, Z Cai, J Shen, S Zhou… - Expert Systems with …, 2023 - Elsevier
Accurate, robust and automatic segmentation of the left atrium (LA) in magnetic resonance
images (MRI) is of great significance for studying the LA structure and facilitating the …
images (MRI) is of great significance for studying the LA structure and facilitating the …
Concatenation of pre-trained convolutional neural networks for enhanced COVID-19 screening using transfer learning technique
Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory
symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the …
symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the …
Explainable machine-learning models for COVID-19 prognosis prediction using clinical, laboratory and radiomic features
The SARS-CoV-2 virus pandemic had devastating effects on various aspects of life: clinical
cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high …
cases, ranging from mild to severe, can lead to lung failure and to death. Due to the high …
Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes
One of the primary challenges in applying deep learning approaches to medical imaging is
the limited availability of data due to various factors. These factors include concerns about …
the limited availability of data due to various factors. These factors include concerns about …
Blockchain-federated and deep-learning-based ensembling of capsule network with incremental extreme learning machines for classification of COVID-19 using CT …
Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy
must be employed to isolate COVID-19. When it comes to determining the identity of COVID …
must be employed to isolate COVID-19. When it comes to determining the identity of COVID …
RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images
Since the advent of COVID-19, the number of deaths has increased exponentially, boosting
the requirement for various research studies that may correctly diagnose the illness at an …
the requirement for various research studies that may correctly diagnose the illness at an …
Application of artificial intelligence in oncology nursing: a scoping review
T Zhou, Y Luo, J Li, H Zhang, Z Meng, W Xiong… - Cancer …, 2022 - journals.lww.com
Background Artificial intelligence (AI) has been increasingly used in healthcare during the
last decade, and recent applications in oncology nursing have shown great potential in …
last decade, and recent applications in oncology nursing have shown great potential in …
CT-based severity assessment for COVID-19 using weakly supervised non-local CNN
Evaluating patient criticality is the foremost step in administering appropriate COVID-19
treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for …
treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for …
Human-inspired spatiotemporal feature extraction and fusion network for weather forecasting
Reliable weather forecasting is quite important for sectors with weather-dependent decision-
making. To capture unknown and complex dependencies from multiple spatiotemporal …
making. To capture unknown and complex dependencies from multiple spatiotemporal …
AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis
Conventional soft clustering algorithms perform well on linearly distributed features, but their
performance degrades on nonlinearly distributed features in high-dimensional space. In this …
performance degrades on nonlinearly distributed features in high-dimensional space. In this …