AIMC Topic: Deep Learning

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Deep learning model to identify homonymous defects on automated perimetry.

The British journal of ophthalmology
BACKGROUND: Homonymous visual field (VF) defects are usually an indicator of serious intracranial pathology but may be subtle and difficult to detect. Artificial intelligence (AI) models could play a key role in simplifying the detection of these def...

Application of deep learning models to detect coastlines and shorelines.

Journal of environmental management
Identifying and monitoring coastlines and shorelines play an important role in coastal erosion assessment around the world. The application of deep learning models was used in this study to detect coastlines and shorelines in Vietnam using high-resol...

DeepRHD: An efficient hybrid feature extraction technique for protein remote homology detection using deep learning strategies.

Computational biology and chemistry
In computational biology, the Protein Remote homology Detection technique (PRHD) has got undeniable significance. It is mostly important for structure and function identification of a protein sequence. The previous years have seen a challenge that la...

A systematic review on machine learning and deep learning techniques in cancer survival prediction.

Progress in biophysics and molecular biology
Cancer is a disease which is characterised by the unusual and uncontrollable growth of body cells. This usually happens asymptomatically and gets spread to other parts of the body. The major problem in treating cancer is that its progress is not moni...

Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.

Neuroradiology
PURPOSE: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.

Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application.

Cancer science
Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have develope...

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach.

Sensors (Basel, Switzerland)
This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during...

Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.

Computational intelligence and neuroscience
The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropaga...

Deep learning for topical trend discovery in online discourse about Pre-Exposure Prophylaxis (PrEP).

AIDS and behavior
Pre-Exposure Prophylaxis (PrEP) interventions are increasingly prevalent on social media. These data can be mined for insights about PrEP that may not be as apparent in surveys including personal musings about PrEP and barriers/facilitators to PrEP u...

Deep learning-guided weighted averaging for signal dropout compensation in DWI of the liver.

Magnetic resonance in medicine
PURPOSE: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.