AIMC Topic: Neural Networks, Computer

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PM concentration estimation using convolutional neural network and gradient boosting machine.

Journal of environmental sciences (China)
Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM) concentration. Here we explo...

Deep learning in interstitial lung disease-how long until daily practice.

European radiology
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung...

Artificial intelligence and radiomics in pediatric molecular imaging.

Methods (San Diego, Calif.)
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving fie...

A neurodynamic optimization approach for complex-variables programming problem.

Neural networks : the official journal of the International Neural Network Society
A neural network model upon differential inclusion is designed for solving the complex-variables convex programming, and the chain rule for real-valued function with the complex-variables is established in this paper. The model does not need to choos...

Missing data imputation with adversarially-trained graph convolutional networks.

Neural networks : the official journal of the International Neural Network Society
Missing data imputation (MDI) is the task of replacing missing values in a dataset with alternative, predicted ones. Because of the widespread presence of missing data, it is a fundamental problem in many scientific disciplines. Popular methods for M...

A machine learning model for detecting invasive ductal carcinoma with Google Cloud AutoML Vision.

Computers in biology and medicine
OBJECTIVES: This study is aimed to assess the feasibility of AutoML technology for the identification of invasive ductal carcinoma (IDC) in whole slide images (WSI).

Convolutional neural network for detection and classification of seizures in clinical data.

Medical & biological engineering & computing
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usual...

Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection.

IEEE transactions on biomedical circuits and systems
There is an unmet need for a low-cost instrumented technology for detecting sanitation-related malodor as an alert for maintenance around shared toilets and emerging technologies for onsite waste treatment. In this article, our approach to an electro...

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound.

Medical image analysis
The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the whole breast anatomy. To facilitate the reading of AWBUS images and support the breast density estimation, an automatic breast anatomy segmentation me...

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging.

Biomedical physics & engineering express
The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, hig...