AIMC Topic: Deep Learning

Clear Filters Showing 11221 to 11230 of 28423 articles

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

Computers in biology and medicine
The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we desig...

Bridging adaptive management and reinforcement learning for more robust decisions.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situat...

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

Radiation oncology (London, England)
BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly cr...

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

Communications biology
Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of de...

A Review Paper about Deep Learning for Medical Image Analysis.

Computational and mathematical methods in medicine
Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatm...

Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI.

NMR in biomedicine
At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no...

Deep learning in optical coherence tomography: Where are the gaps?

Clinical & experimental ophthalmology
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, in...

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors.

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Physical and engineering sciences in medicine
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective ...

Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

Sensors (Basel, Switzerland)
It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usabilit...