AIMC Topic: Neural Networks, Computer

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Learning Hierarchical Representations with Spike-and-Slab Inception Network.

Sensors (Basel, Switzerland)
Recently, deep convolutional neural networks (CNN) with inception modules have attracted much attention due to their excellent performances on diverse domains. Nevertheless, the basic CNN can only capture a univariate feature, which is essentially li...

Face with Mask Detection in Thermal Images Using Deep Neural Networks.

Sensors (Basel, Switzerland)
As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it p...

Visual explanations from spiking neural networks using inter-spike intervals.

Scientific reports
By emulating biological features in brain, Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional deep learning. To make SNNs ubiquitous, a 'visual explanation' technique for analysing and explaining the internal spike b...

Robust whole slide image analysis for cervical cancer screening using deep learning.

Nature communications
Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinica...

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study.

European radiology experimental
BACKGROUND: Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed t...

Imaging depth adaptive resolution enhancement for optical coherence tomography via deep neural network with external attention.

Physics in medicine and biology
Optical coherence tomography (OCT) is a promising non-invasive imaging technique that owns many biomedical applications. In this paper, a deep neural network is proposed for enhancing the spatial resolution of OCTimages. Different from the previous r...

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.

Frontiers in public health
Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implem...

Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm.

Computational intelligence and neuroscience
Urban landscape planning and design is not only closely related to people's living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent c...

Disturbance-immune weight sharing for neural architecture search.

Neural networks : the official journal of the International Neural Network Society
Neural architecture search (NAS) has gained increasing attention in the community of architecture design. One of the key factors behind the success lies in the training efficiency brought by the weight sharing (WS) technique. However, WS-based NAS me...

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...