AIMC Topic: Deep Learning

Clear Filters Showing 14801 to 14810 of 28423 articles

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs.

MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images.

Medical image analysis
Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. Howeve...

Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework.

Cells
As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its ...

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.

International journal of radiation oncology, biology, physics
PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work...

Intracerebral hemorrhage detection on computed tomography images using a residual neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically da...

Automatic head computed tomography image noise quantification with deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Computed tomography (CT) image noise is usually determined by standard deviation (SD) of pixel values from uniform image regions. This study investigates how deep learning (DL) could be applied in head CT image noise estimation.

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning.

Scientific reports
We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. ...

Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.

BMC cancer
BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic...

Identifying pathological slices of gastric cancer via deep learning.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND: The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, ...