Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data
Sign Up to like & getrecommendations! Published in 2022 at "Epidemiology"
DOI: 10.1097/ede.0000000000001578
Abstract: Background: Missing data are common in studies using electronic health records (EHRs)-derived data. Missingness in EHR data is related to healthcare utilization patterns, resulting in complex and potentially missing not at random missingness mechanisms. Prior… read more here.
Keywords: missingness; random; denoising autoencoders; electronic health ... See more keywords
Denoising Autoencoders for Laser-Based Scan Registration
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2018.2867856
Abstract: In this letter, we build on recent advances in deep learning to improve SE(3) transformations, enabling more accurate motion estimation in mobile robots. We propose using denoising autoencoders (DAEs) to address the challenges presented by… read more here.
Keywords: scan registration; denoising autoencoders; motion; based scan ... See more keywords
Deinterleaving of Pulse Streams With Denoising Autoencoders
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Aerospace and Electronic Systems"
DOI: 10.1109/taes.2020.3004208
Abstract: Analyzing radar signals is an important task in operating electronic support measure systems. The received signals in the real electromagnetic environment often originate from multiple emitters and must be separated for further processing. Pulses from… read more here.
Keywords: streams denoising; pulse streams; denoising autoencoders; deinterleaving pulse ... See more keywords
Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2020.2978440
Abstract: Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there… read more here.
Keywords: mitigation wall; radar images; denoising autoencoders; frontal radar ... See more keywords
Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Medical Imaging"
DOI: 10.1117/1.jmi.4.4.041311
Abstract: Abstract. The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with… read more here.
Keywords: lesion detection; denoising autoencoders; segmentation; brain ... See more keywords
Hyperspectral anomaly detection based on stacked denoising autoencoders
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Applied Remote Sensing"
DOI: 10.1117/1.jrs.11.042605
Abstract: Abstract. Hyperspectral anomaly detection (AD) is an important technique of unsupervised target detection and has significance in real situations. Due to the high dimensionality of hyperspectral data, AD will be influenced by noise, nonlinear correlation… read more here.
Keywords: denoising autoencoders; detection; feature; anomaly detection ... See more keywords