AIMC Topic: Neural Networks, Computer

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Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BMC infectious diseases
BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.

A sparse quantized hopfield network for online-continual memory.

Nature communications
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in t...

Self-Supervised Lightweight Depth Estimation in Endoscopy Combining CNN and Transformer.

IEEE transactions on medical imaging
In recent years, an increasing number of medical engineering tasks, such as surgical navigation, pre-operative registration, and surgical robotics, rely on 3D reconstruction techniques. Self-supervised depth estimation has attracted interest in endos...

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

IEEE transactions on medical imaging
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated grea...

A Test Statistic Estimation-Based Approach for Establishing Self-Interpretable CNN-Based Binary Classifiers.

IEEE transactions on medical imaging
Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but ...

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledg...

Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays.

IEEE transactions on neural networks and learning systems
Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SN...

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

IEEE transactions on neural networks and learning systems
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...

A calculation method for optical properties of yolk shell based on deep learning.

PloS one
The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. ...

Attentional adversarial training for few-shot medical image segmentation without annotations.

PloS one
Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a sc...