AIMC Topic: Neural Networks, Computer

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Microaneurysm detection using fully convolutional neural networks.

Computer methods and programs in biomedicine
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automat...

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

Journal of clinical microbiology
Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural net...

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

Neural networks : the official journal of the International Neural Network Society
The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive cri...

Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher.

Neural networks : the official journal of the International Neural Network Society
Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In...

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products.

Water research
The use of fluorescence data coupled with neural networks for improved predictability of drinking water disinfection by-products (DBPs) was investigated. Novel application of autoencoders to process high-dimensional fluorescence data was related to c...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Medical image analysis
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care...

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

Medical image analysis
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-tas...

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Artificial intelligence in medicine
Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, w...

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

IEEE journal of biomedical and health informatics
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the...

Biologically Inspired Intensity and Depth Image Edge Extraction.

IEEE transactions on neural networks and learning systems
In recent years, artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low-cost depth cameras. However, depth images require a lot ...