AIMC Topic: Deep Learning

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A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Nature communications
Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce ...

Estimating muscle activation from EMG using deep learning-based dynamical systems models.

Journal of neural engineering
. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent var...

Multimodal Vigilance Estimation Using Deep Learning.

IEEE transactions on cybernetics
The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that cons...

Deep Learning Method for Grasping Novel Objects Using Dexterous Hands.

IEEE transactions on cybernetics
Robotic grasping ability lags far behind human skills and poses a significant challenge in the robotics research area. According to the grasping part of an object, humans can select the appropriate grasping postures of their fingers. When humans gras...

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

Computational intelligence and neuroscience
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this w...

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches.

PeerJ
An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September...

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

PloS one
Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield...

Predicting Perceived Reporting Complexity of Abdominopelvic Computed Tomography With Deep Learning.

Journal of computer assisted tomography
OBJECTIVE: The purpose of this pilot study was to examine human and automated estimates of reporting complexity for computed tomography (CT) studies of the abdomen and pelvis.

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

Magma (New York, N.Y.)
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic reson...

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.

Diagnostic and interventional imaging
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...