AIMC Topic: Deep Learning

Clear Filters Showing 5911 to 5920 of 28423 articles

Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model.

Analytical and bioanalytical chemistry
Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identificat...

Cross-view discrepancy-dependency network for volumetric medical image segmentation.

Medical image analysis
The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (i.e., multi-view slices) for alleviating this issue. However, such method...

Detection and classification of electrocardiography using hybrid deep learning models.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
OBJECTIVE: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to ex...

Tracking perching behavior of cage-free laying hens with deep learning technologies.

Poultry science
Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure...

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the as...

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

International journal of cardiology
BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combi...

A Federated Learning Protocol for Spiking Neural Membrane Systems.

International journal of neural systems
Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consume...

Forecasting the incidence frequencies of schizophrenia using deep learning.

Asian journal of psychiatry
Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting is crucial for effective mental health strategies. This study developed a long short-term memory (LSTM)-based recurrent neural network model to predict...

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

Computer methods and programs in biomedicine
BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and system...

Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.

Surgical endoscopy
BACKGROUND: There is an increasing demand for automated surgical skill assessment to solve issues such as subjectivity and bias that accompany manual assessments. This study aimed to verify the feasibility of assessing surgical skills using a surgica...