AIMC Topic: Deep Learning

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Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement.

Scientific reports
Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build...

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study.

Journal of medical Internet research
BACKGROUND: Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorpor...

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis.

Journal of healthcare engineering
In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm f...

Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection.

Journal of healthcare engineering
This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, c...

Structure-based protein design with deep learning.

Current opinion in chemical biology
Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to u...

Imaging in focus: An introduction to denoising bioimages in the era of deep learning.

The international journal of biochemistry & cell biology
Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the ...

Robustifying Deep Networks for Medical Image Segmentation.

Journal of digital imaging
The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robus...

Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness.

IEEE transactions on bio-medical engineering
OBJECTIVE: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth a...

A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children With Cleft Palate.

IEEE transactions on bio-medical engineering
OBJECTIVES: Evaluation of hypernasality requires extensive perceptual training by clinicians and extending this training on a large scale internationally is untenable; this compounds the health disparities that already exist among children with cleft...

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.

Sensors (Basel, Switzerland)
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by ...