AIMC Topic: Deep Learning

Clear Filters Showing 18941 to 18950 of 28423 articles

Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity.

The British journal of radiology
OBJECTIVE: Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to chemotherapy for breast cancer. This study investigated an ...

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

EBioMedicine
BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for EUS BD scanning augmentation.

Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images.

Nature communications
It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learn...

Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning.

Biomedical physics & engineering express
Replacing CT imaging with MR imaging for MR-only radiotherapy has sparked the interest of many scientists and is being increasingly adopted in radiation oncology. Although many studies have focused on generating CT images from MR images, only models ...

A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks.

Physics in medicine and biology
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinic...

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnos...

Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information.

Food chemistry
A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and ref...

Convergence of the RMSProp deep learning method with penalty for nonconvex optimization.

Neural networks : the official journal of the International Neural Network Society
A norm version of the RMSProp algorithm with penalty (termed RMSPropW) is introduced into the deep learning framework and its convergence is addressed both analytically and numerically. For rigour, we consider the general nonconvex setting and prove ...

The future of CT: deep learning reconstruction.

Clinical radiology
There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstructi...

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures.

Cell research
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/o...