deep learning

: Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks

Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks
Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks 750 422 IEEE Transactions on Biomedical Engineering (TBME)
Deep learning model decodes surface electromyographic signals into proportional hand movements, accurately controlling both individual finger and compound movements, with potential to enhance intuitive interfaces for assistive hand devices. read more
Characterization of retinal arteries by adaptive optics ophthalmoscopy image analysis

Characterization of retinal arteries by adaptive optics ophthalmoscopy image analysis
Characterization of retinal arteries by adaptive optics ophthalmoscopy image analysis 710 400 IEEE Transactions on Biomedical Engineering (TBME)
We present a complete framework for the segmentation of Adaptive optics ophthalmoscopy (AOO) images and the estimation of retinal vascular biomarkers, based on deep-learning and dedicated active contour models. read more
The Deep-Match Framework: R-Peak Detection in Ear-ECG

The Deep-Match Framework: R-Peak Detection in Ear-ECG
The Deep-Match Framework: R-Peak Detection in Ear-ECG 789 444 IEEE Transactions on Biomedical Engineering (TBME)
In this work we created an efficient and interpretable deep-learning “matched filter” for precise R-peak detection in wearable ear-ECG signals with poor signal-to-noise ratios. read more
Diagnosis of Coexisting Valvular Heart Diseases Using Image-to-Sequence Translation of Contact Microphone Recordings

Diagnosis of Coexisting Valvular Heart Diseases Using Image-to-Sequence Translation of Contact Microphone Recordings
Diagnosis of Coexisting Valvular Heart Diseases Using Image-to-Sequence Translation of Contact Microphone Recordings 631 355 IEEE Transactions on Biomedical Engineering (TBME)
This study establishes a connection between abnormal heart sounds and the presence of coexisting valvular heart diseases, laying the foundation for wearable, rapid decision-making on heart valve health status. read more
Dynamic Viewing Pattern Analysis: Towards Large-Scale Screening of Children with ASD in Remote Areas

Dynamic Viewing Pattern Analysis: Towards Large-Scale Screening of Children with ASD in Remote Areas
Dynamic Viewing Pattern Analysis: Towards Large-Scale Screening of Children with ASD in Remote Areas 789 444 IEEE Transactions on Biomedical Engineering (TBME)
This study has discovered an effective viewing feature for identifying children with ASD and developed an intelligent classification model. It provides powerful support for low-cost and non-invasive ASD screening. read more
A Robotic System With Embedded Open Microfluidic Chip for Automatic Embryo Vitrification

Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT
Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT 795 411 IEEE Transactions on Biomedical Engineering (TBME)
This is the first study to demonstrate direct visualization and automated quantification of 3D subsurface microstructures in small airways in vivo with a novel deep-learning assisted diffractive optical coherence tomography. read more

Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations
Author(s)3: Mike Lind Rank, Preben Kidmose
Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations IEEE Transactions on Biomedical Engineering (TBME)
We combine ear-EEG sleep recordings with a state-of-the-art sleep scoring model, ‘seqsleepnet’, to investigate the upper limits of mobile sleep scoring. We manage to further improve on the state of the art in this field, and perform a detailed analysis of the influence of electrode positioning. From this, we find a general rule of thumb that as long a data set contain EOG information and electrode distance on the order of the width of the head, then good automatic sleep scoring is possible. We also find indications that the obtained automatic scoring may be more reliable than the manual scoring. read more

Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors
Author(s)3: Blake Zimmerman, Sara Johnson, Henrik Odéen, Jill Shea, Markus D. Foote, Nicole Winkler, Sarang Joshi, Allison Payne
Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors 170 177 IEEE Transactions on Biomedical Engineering (TBME)
A significant challenge for noninvasive MR-guided focused ultrasound (MRgFUS) treatment is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses contrast agents that prevent continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for iterative assessment with inhibiting treatment continuation. Trained using a novel volume-conserving registration algorithm, the presented noncontrast biomarker outperformed the current clinical standard on a VX2 rabbit tumor model. Details on the registration and deep learning model are included. read more

Mammography Image Quality Assurance Using Deep Learning
Author(s)3: Tobias Kretz, Klaus-Robert Müller, Tobias Schaeffter, Clemens Elster
Mammography Image Quality Assurance Using Deep Learning 170 177 IEEE Transactions on Biomedical Engineering (TBME)
Image quality assurance is crucial in mammography to ensure reliable breast cancer diagnostics. Analyzing images of a technical phantom allows to routinely and reliably assess image quality. Current state-of-the-art analysis determines local image quality features by applying pre-processing and regression procedures for a set of repeatedly recorded images. This proof of concept paper demonstrates that mammography image quality assessment can benefit from deep learning. A neural network is trained on a large database of phantom images, and it is shown that the trained net retrieves the local image quality features already from single images without cumbersome pre-processing. This allows to maintain quality standards at significantly less labor. read more

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound
Author(s)3: Micha Feigin, Brian W. Anthony, Daniel Freedman
A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Abnormalities in the tissue’s mechanical properties and structure, as well as their spatial arrangement, are useful in disease diagnosis and monitoring of disease progression. To this end, ultrasound shear wave…

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