AIMC Topic: Neural Networks, Computer

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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...

Optimal length of R-R interval segment window for Lorenz plot detection of paroxysmal atrial fibrillation by machine learning.

Biomedical engineering online
BACKGROUND: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detect...

Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks.

Translational vision science & technology
PURPOSE: To develop a neural network (NN)-based approach, with limited training resources, that identifies and counts the number of retinal pigment epithelium (RPE) cells in confocal microscopy images obtained from cell culture or mice RPE/choroid fl...

Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
We study training deep neural network (DNN) frequency-domain beamformers using simulated and phantom anechoic cysts and compare to training with simulated point target responses. Using simulation, physical phantom, and in vivo scans, we find that tra...

Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO-P25: optimization and modeling approach.

Environmental science and pollution research international
The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove orga...

Internet of things-inspired healthcare system for urine-based diabetes prediction.

Artificial intelligence in medicine
Healthcare industry is the leading domain that has been revolutionized by the incorporation of Internet of Things (IoT) technology resulting in smart medical applications. Conspicuously, this study presents an effective system of home-centric Urine-b...

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

Nature methods
An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule l...

Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Recent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in t...

A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements.

Scientific reports
Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tom...

Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.

eNeuro
Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean=0.95, SD<0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipu...