AIMC Topic: Neural Networks, Computer

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Empirical modeling and prediction of neuronal dynamics.

Biological cybernetics
Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although ...

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.

Frontiers in public health
PURPOSE: Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence.

From explanation to intervention: Interactive knowledge extraction from Convolutional Neural Networks used in radiology.

PloS one
Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they fai...

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

Ultraschall in der Medizin (Stuttgart, Germany : 1980)
PURPOSE: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis.

Deep causal learning for pancreatic cancer segmentation in CT sequences.

Neural networks : the official journal of the International Neural Network Society
Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current deep-learning (DL) methods usually segment the pancreas or tumor independently using mixed image fe...

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.

Medical image analysis
Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse ...

Expressive power of ReLU and step networks under floating-point operations.

Neural networks : the official journal of the International Neural Network Society
The study of the expressive power of neural networks has investigated the fundamental limits of neural networks. Most existing results assume real-valued inputs and parameters as well as exact operations during the evaluation of neural networks. Howe...

Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches.

Chemosphere
Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computation...

Predicting lysine methylation sites using a convolutional neural network.

Methods (San Diego, Calif.)
Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins. Deregulation caused by lysine methyltransferases has been identified as t...

DesTrans: A medical image fusion method based on Transformer and improved DenseNet.

Computers in biology and medicine
Medical image fusion can provide doctors with more detailed data and thus improve the accuracy of disease diagnosis. In recent years, deep learning has been widely used in the field of medical image fusion. The traditional method of medical image fus...