AIMC Topic: Neural Networks, Computer

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An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module.

Sensors (Basel, Switzerland)
Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment ...

Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping.

Sensors (Basel, Switzerland)
An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing...

LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing.

Sensors (Basel, Switzerland)
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the d...

The neural coding framework for learning generative models.

Nature communications
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the...

Memristor-based analogue computing for brain-inspired sound localization with in situ training.

Nature communications
The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different fro...

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.

BMC bioinformatics
BACKGROUND: Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality o...

Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN).

Diagnostic pathology
BACKGROUND: Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI).

Deep Learning-Based Ultrasound Combined with Gastroscope for the Diagnosis and Nursing of Upper Gastrointestinal Submucous Lesions.

Computational and mathematical methods in medicine
The study focused on the diagnostic value of deep learning-based ultrasound combined with gastroscope examination for upper gastrointestinal submucous lesions and nursing. A total of 104 patients with upper gastrointestinal submucous lesions diagnose...

Predicting diarrhoea outbreaks with climate change.

PloS one
BACKGROUND: Climate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, diseases related to diarrhoea outbreak is a leading cause of morbidity ...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

Computers in biology and medicine
The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of in...