AIMC Topic: Neural Networks, Computer

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Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer.

World journal of gastroenterology
The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis...

Machine learning-based outcome prediction and novel hypotheses generation for substance use disorder treatment.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a nove...

Skip-Connected Self-Recurrent Spiking Neural Networks With Joint Intrinsic Parameter and Synaptic Weight Training.

Neural computation
As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two pro...

Using large-scale experiments and machine learning to discover theories of human decision-making.

Science (New York, N.Y.)
Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this go...

A deep learning approach to dental restoration classification from bitewing and periapical radiographs.

Quintessence international (Berlin, Germany : 1985)
OBJECTIVE: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radio...

[Application of deep learning neural network in pathological image classification of non-inflammatory aortic membrane degeneration].

Zhonghua bing li xue za zhi = Chinese journal of pathology
To investigate the value of deep learning in classifying non-inflammatory aortic membrane degeneration. Eighty-nine cases of non-inflammatory aortic media degeneration diagnosed from January to June 2018 were collected at Beijing Anzhen Hospital, C...

A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks.

Mathematical biosciences and engineering : MBE
Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identi...

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.

World journal of gastroenterology
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) d...

A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography.

The Review of scientific instruments
Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neu...

Robust North Atlantic right whale detection using deep learning models for denoising.

The Journal of the Acoustical Society of America
This paper proposes a robust system for detecting North Atlantic right whales by using deep learning methods to denoise noisy recordings. Passive acoustic recordings of right whale vocalisations are subject to noise contamination from many sources, s...