[HTML][HTML] Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images
Detection of abnormalities within the inner ear is a challenging task even for experienced
clinicians. In this study, we propose an automated method for automatic abnormality …
clinicians. In this study, we propose an automated method for automatic abnormality …
Deep reinforcement learning for detection of inner ear abnormal anatomy in computed tomography
Detection of abnormalities within the inner ear is a challenging task that, if automated, could
provide support for the diagnosis and clinical management of various otological disorders …
provide support for the diagnosis and clinical management of various otological disorders …
Deep reinforcement learning for detection of abnormal anatomies
Automatic detection of abnormal anatomies or malformations of different structures of the
human body is a challenging task that could provide support for clinicians in their daily …
human body is a challenging task that could provide support for clinicians in their daily …
Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images
M Ogawa, M Kisohara, T Yamamoto, S Shibata… - Computers in Biology …, 2022 - Elsevier
Background and purpose To examine the diagnostic performance of unsupervised deep
learning using a 3D variational autoencoder (VAE) for detecting and localizing inner ear …
learning using a 3D variational autoencoder (VAE) for detecting and localizing inner ear …
Accurate detection of inner ears in head CTs using a deep volume-to-volume regression network with false positive suppression and a shape-based constraint
Cochlear implants (CIs) are neural prosthetics which are used to treat patients with hearing
loss. CIs use an array of electrodes which are surgically inserted into the cochlea to …
loss. CIs use an array of electrodes which are surgically inserted into the cochlea to …
Unsupervised classification of congenital inner ear malformations using DeepDiffusion for latent space representation
The identification of congenital inner ear malformations is a challenging task even for
experienced clinicians. In this study, we present the first automated method for classifying …
experienced clinicians. In this study, we present the first automated method for classifying …
[HTML][HTML] A novel radiological software prototype for automatically detecting the inner ear and classifying normal from malformed anatomy
AA Almansi, S Sugarova, A Alsanosi… - Computers in biology …, 2024 - Elsevier
Background To develop an effective radiological software prototype that could read Digital
Imaging and Communications in Medicine (DICOM) files, crop the inner ear automatically …
Imaging and Communications in Medicine (DICOM) files, crop the inner ear automatically …
Automatic detection of the inner ears in head CT images using deep convolutional neural networks
Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to
stimulate nerve endings to replace the natural electro-mechanical transduction mechanism …
stimulate nerve endings to replace the natural electro-mechanical transduction mechanism …
Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network
Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear
such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and …
such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and …
Identifying ear abnormality from 2D photographs using convolutional neural networks
Quantifying ear deformity using linear measurements and mathematical modeling is difficult
due to the ear's complex shape. Machine learning techniques, such as convolutional neural …
due to the ear's complex shape. Machine learning techniques, such as convolutional neural …
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