AIMC Topic: Deep Learning

Clear Filters Showing 20061 to 20070 of 28423 articles

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images.

The British journal of ophthalmology
BACKGROUND/AIMS: Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angl...

Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

Virchows Archiv : an international journal of pathology
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...

Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.

Surgery
BACKGROUND: Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision ...

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.

Neuroinformatics
The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma le...

Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic bilateral stenosis of the internal carotid arteries and by the formation of an abnormal vascular network called moyamoya vessels. In this stury, the au...

High-performance rapid MR parameter mapping using model-based deep adversarial learning.

Magnetic resonance imaging
PURPOSE: To develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.

Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm.

European journal of radiology
PURPOSE: To develop and evaluate an automatic measurement model for hip joints based on anteroposterior (AP) pelvic radiography and a deep learning algorithm.

Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular a...

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach.

Environmental science & technology
A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibil...

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning.

Sensors (Basel, Switzerland)
A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activ...