AIMC Topic: Deep Learning

Clear Filters Showing 22461 to 22470 of 28423 articles

Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions.

Scientific reports
The diagnosis of pancreatic cystic lesions remains challenging. This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CEA), cytology, and artificial intelligence (AI) by deep learning using cyst fluid in differentiating ...

Defining Image Memorability Using the Visual Memory Schema.

IEEE transactions on pattern analysis and machine intelligence
Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The...

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similar...

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

The Journal of arthroplasty
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.

Magnetic resonance in medicine
PURPOSE: We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization.

BoSR: A CNN-based aurora image retrieval method.

Neural networks : the official journal of the International Neural Network Society
The deep learning models especially the CNN have achieved amazing performance on natural image retrieval. However, remote sensing images captured with anamorphic lens are still retrieved via manual selection or traditional SIFT-based methods. How to ...

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Psychological assessment
Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the ...

A review of automated sleep stage scoring based on physiological signals for the new millennia.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiologic...