AIMC Topic: Neural Networks, Computer

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The applications of deep learning algorithms on in silico druggable proteins identification.

Journal of advanced research
INTRODUCTION: The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of nov...

Contour proposal networks for biomedical instance segmentation.

Medical image analysis
We present a conceptually simple framework for object instance segmentation, called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using a fixed-size represen...

Distortion and instability compensation with deep learning for rotational scanning endoscopic optical coherence tomography.

Medical image analysis
Optical Coherence Tomography (OCT) is increasingly used in endoluminal procedures since it provides high-speed and high resolution imaging. Distortion and instability of images obtained with a proximal scanning endoscopic OCT system are significant d...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

Journal of cellular and molecular medicine
There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death,...

Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.

Pediatric radiology
BACKGROUND: Germinal matrix hemorrhage-intraventricular hemorrhage is among the most common intracranial complications in premature infants. Early detection is important to guide clinical management for improved patient prognosis.

Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting.

Sensors (Basel, Switzerland)
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods ...

Stretching Method-Based Damage Detection Using Neural Networks.

Sensors (Basel, Switzerland)
We present in this paper a framework for damage detection and localization using neural networks. The data we use to train the network are m×d pixel images consisting of measurements of the relative variations of natural frequencies of the structure...

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

BioMed research international
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any ...

Research on Management Efficiency and Dynamic Relationship in Intelligent Management of Tourism Engineering Based on Industry 4.0.

Computational intelligence and neuroscience
The digital age of artificial intelligence marks the rapid development of tourism engineering and the gradual improvement of intelligent management theory. This study aims to solve the problems of low efficiency of dynamic relationship analysis and l...

Sub-micro scale cell segmentation using deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delineation of newly revealed cellular features at the submicron through nanoscale becomes important. Relia...