AIMC Topic: Neural Networks, Computer

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Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.

Cerebral cortex (New York, N.Y. : 1991)
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models.

International journal of mycobacteriology
BACKGROUND: Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Under the Algorithmic Microscope.

IEEE computer graphics and applications
Through the use of generative adversarial networks (GANs), Berlin-based artist Sofia Crespo creates artificial yet realistic biological ecosystems filled with creatures that look like insects, jellyfish, or other complicated organisms. Albertus Seba'...

New Avenues in Audio Intelligence: Towards Holistic Real-life Audio Understanding.

Trends in hearing
Computer audition (i.e., intelligent audio) has made great strides in recent years; however, it is still far from achieving holistic hearing abilities, which more appropriately mimic human-like understanding. Within an audio scene, a human listener i...

Automated Identification of Dental Implants Using Artificial Intelligence.

The International journal of oral & maxillofacial implants
PURPOSE: To develop and evaluate the accuracy of a computer-assisted system based on artificial intelligence for detecting and identifying dental implant brands using digital periapical radiographs.

Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery.

Technology in cancer research & treatment
The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a deep learning algorithm for Gamma Knife (GK) stereotactic radiosurgery (SRS). The Monte Carlo (MC) m...

Deep Learning: An Update for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image fea...

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this condition is difficult to provide because the clinical evaluation of radiation dermatitis is made by...

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.

Technology in cancer research & treatment
Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-de...

Review of wheeled mobile robot collision avoidance under unknown environment.

Science progress
Recently, the working scenes of the robot have been emerging as diversity and complexity with gradually mature of robotic control technology. The challenge of robot adaptability emerges, especially in complicated and unknown environments. Among the n...