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Deep learning on medical image analysis
Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
various diseases. Convolutional neural networks (CNNs) have become popular as they can …
various diseases. Convolutional neural networks (CNNs) have become popular as they can …
Exploring multiple instance learning (MIL): A brief survey
Abstract Multiple Instance Learning (MIL) is a learning paradigm, where training instances
are arranged in sets, called bags, and only bag-level labels are available during training …
are arranged in sets, called bags, and only bag-level labels are available during training …
DPMSN: A dual-pathway multiscale network for image forgery detection
Multimedia images have become an important way for the sharing of digital information.
However, advanced editing tools provide easy methods for malicious content modification …
However, advanced editing tools provide easy methods for malicious content modification …
Bearing fault diagnosis via fusing small samples and training multi-state Siamese neural networks
Recently, deep learning techniques have been widely applied to fault diagnosis due to their
outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis …
outstanding feature extraction abilities. The success of deep-learning-based fault diagnosis …
A novel local binary temporal convolutional neural network for bearing fault diagnosis
In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault
signal collection. Considering the excessive parameters in the traditional convolutional …
signal collection. Considering the excessive parameters in the traditional convolutional …
A deep learning approach for electric motor fault diagnosis based on modified InceptionV3
Electric motors are essential equipment widely employed in various sectors. However,
factors such as prolonged operation, environmental conditions, and inadequate …
factors such as prolonged operation, environmental conditions, and inadequate …
[HTML][HTML] Enhancing the super-resolution of medical images: Introducing the deep residual feature distillation channel attention network for optimized performance and …
In the advancement of medical image super-resolution (SR), the Deep Residual Feature
Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This …
Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This …
A survey on privacy-preserving control and filtering of networked control systems
With the increasing utilisation of information technology and artificial intelligence in practical
control systems, particularly in large-scale distributed networked systems, growing concerns …
control systems, particularly in large-scale distributed networked systems, growing concerns …
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …
improve learning of generalizable synthesis models that reliably translate source-onto target …
Zonotope-based distributed set-membership fusion estimation for artificial neural networks under the dynamic event-triggered mechanism
This article is concerned with the distributed set-membership fusion estimation problem for a
class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism …
class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism …