AIMC Topic: Neural Networks, Computer

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Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

International ophthalmology
PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system.

Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks.

Medical image analysis
This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgi...

Neural network control of focal position during time-lapse microscopy of cells.

Scientific reports
Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experimen...

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Scientific reports
Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learnin...

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

International journal of health geographics
BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative s...

Vector-based navigation using grid-like representations in artificial agents.

Nature
Deep neural networks have achieved impressive successes in fields ranging from object recognition to complex games such as Go. Navigation, however, remains a substantial challenge for artificial agents, with deep neural networks trained by reinforcem...

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases.

Journal of healthcare engineering
Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper...

Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators.

PloS one
Social and economic systems produce complex and nonlinear relationships in the indicator variables that describe them. We present a Bayesian methodology to analyze the dynamical relationships between indicator variables by identifying the nonlinear f...

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images.

PloS one
In pathology, Immunohistochemical staining (IHC) of tissue sections is regularly used to diagnose and grade malignant tumors. Typically, IHC stain interpretation is rendered by a trained pathologist using a manual method, which consists of counting e...

Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.

Computers in biology and medicine
Osteoporotic vertebral fractures (OVFs) are prevalent in older adults and are associated with substantial personal suffering and socio-economic burden. Early diagnosis and treatment of OVFs are critical to prevent further fractures and morbidity. How...