AIMC Topic: Deep Learning

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Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation.

IEEE journal of biomedical and health informatics
Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their daily function and rehabilitation progress, as well as providing a convenient and practical solution ...

Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI.

NeuroImage. Clinical
Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of...

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism.

PloS one
Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incid...

Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering.

Neural networks : the official journal of the International Neural Network Society
Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of ...

A multiscale 3D network for lung nodule detection using flexible nodule modeling.

Medical physics
BACKGROUND: Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant ris...

Selection of Convolutional Neural Network Model for Bladder Tumor Classification of Cystoscopy Images and Comparison with Humans.

Journal of endourology
An investigation of various convolutional neural network (CNN)-based deep learning algorithms was conducted to select the appropriate artificial intelligence (AI) model for calculating the diagnostic performance of bladder tumor classification on cy...

A deep-learning-based threshold-free method for automated analysis of rodent behavior in the forced swim test and tail suspension test.

Journal of neuroscience methods
BACKGROUND: The forced swim test (FST) and tail suspension test (TST) are widely used to assess depressive-like behaviors in animals. Immobility time is used as an important parameter in both FST and TST. Traditional methods for analyzing FST and TST...

A graph neural network approach for predicting drug susceptibility in the human microbiome.

Computers in biology and medicine
Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastroi...

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.

Accident; analysis and prevention
Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, an...