AIMC Topic: Deep Learning

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Identifying Aortic Stenosis With a Single Parasternal Long-Axis Video Using Deep Learning.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

Classification of peripheral blood neutrophils using deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Deep learning has been used to classify the while blood cells in peripheral blood smears. However, the classification of developing neutrophils is rarely studied. Moreover, it is still unknown whether deep learning can work well on the data coming fr...

SAM-X: sorting algorithm for musculoskeletal x-ray radiography.

European radiology
OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity.

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

European radiology
OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to ...

A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools...

Liver lesion changes analysis in longitudinal CECT scans by simultaneous deep learning voxel classification with SimU-Net.

Medical image analysis
The identification and quantification of liver lesions changes in longitudinal contrast enhanced CT (CECT) scans is required to evaluate disease status and to determine treatment efficacy in support of clinical decision-making. This paper describes a...

Isotropic reconstruction for electron tomography with deep learning.

Nature communications
Cryogenic electron tomography (cryoET) allows visualization of cellular structures in situ. However, anisotropic resolution arising from the intrinsic "missing-wedge" problem has presented major challenges in visualization and interpretation of tomog...

Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network.

Journal of neuroscience methods
BACKGROUND: The demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method.