AIMC Topic: Deep Learning

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Detector shifting and deep learning based ring artifact correction method for low-dose CT.

Medical physics
BACKGROUND: In x-ray computed tomography (CT), the gain inconsistency of detector units leads to ring artifacts in the reconstructed images, seriously destroys the image structure, and is not conducive to image recognition. In addition, to reduce rad...

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Journal of digital imaging
Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed to investigate the effect of contrast level and image format on the effectiveness of deep learning fo...

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

European radiology
OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.

Automatic dental biofilm detection based on deep learning.

Journal of clinical periodontology
AIM: To estimate the automated biofilm detection capacity of the U-Net neural network on tooth images.

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments.

International journal of medical informatics
OBJECTIVE: Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and va...

On stars and spikes: Resolving the skeletal morphology of planktonic Acantharia using synchrotron X-ray nanotomography and deep learning image segmentation.

Acta biomaterialia
Acantharia (Acantharea) are wide-spread marine protozoa, presenting one of the rare examples of strontium sulfate mineralization in the biosphere. Their endoskeletons consist of 20 spicules arranged according to a unique geometric pattern named Mülle...

DNA Origami Nanostructure Detection and Yield Estimation Using Deep Learning.

ACS synthetic biology
DNA origami is a milestone in DNA nanotechnology. It is robust and efficient in constructing arbitrary two- and three-dimensional nanostructures. The shape and size of origami structures vary. To characterize them, an atomic force microscope, a trans...

Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods.

Dento maxillo facial radiology
OBJECTIVES: A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (...

A Deep Learning Method Using Gender-Specific Features for Emotion Recognition.

Sensors (Basel, Switzerland)
Speech reflects people's mental state and using a microphone sensor is a potential method for human-computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affect...

CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.

Sensors (Basel, Switzerland)
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both ...