AIMC Topic: Deep Learning

Clear Filters Showing 23251 to 23260 of 28423 articles

Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.

Sensors (Basel, Switzerland)
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a m...

Image set for deep learning: field images of maize annotated with disease symptoms.

BMC research notes
OBJECTIVES: Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers' fields. However, it is difficult for a simple algorithm to distinguish between the target disease and...

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.

IEEE transactions on medical imaging
Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys ...

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

European radiology
OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine ...

Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.

IEEE transactions on medical imaging
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we prop...

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

ACS chemical neuroscience
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein...

A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study.

Journal of medical Internet research
BACKGROUND: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic.

Deep learning for undersampled MRI reconstruction.

Physics in medicine and biology
This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the...