AIMC Topic: Neural Networks, Computer

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Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR...

The Application of Artificial Intelligence to Acoustic Data in Otolaryngology.

Otolaryngologic clinics of North America
Artificial intelligence (AI), particularly deep learning, has revolutionized various fields through its ability to model complex, noisy systems with high accuracy. Driven by advancements in deep neural networks (DNNs), hardware, and data digitization...

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters.

Science bulletin
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep lea...

Improved quantitative parameter estimation for prostate T relaxometry using convolutional neural networks.

Magma (New York, N.Y.)
OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) f...

Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.

Sensors (Basel, Switzerland)
This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This...

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.

Scientific reports
Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and p...

Convolutional neural network advances in demosaicing for fluorescent cancer imaging with color-near-infrared sensors.

Journal of biomedical optics
SIGNIFICANCE: Single-chip imaging devices featuring vertically stacked photodiodes and pixelated spectral filters are advancing multi-dye imaging methods for cancer surgeries, though this innovation comes with a compromise in spatial resolution. To m...

Deep learning for accelerated and robust MRI reconstruction.

Magma (New York, N.Y.)
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstructi...

Multi-view heterogeneous graph learning with compressed hypergraph neural networks.

Neural networks : the official journal of the International Neural Network Society
Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to improve compatibility prediction. However, existing graph-based multi-view learning approaches are ...

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

Neural networks : the official journal of the International Neural Network Society
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency infor...