AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Pattern Recognition, Automated

Showing 11 to 20 of 1638 articles

Clear Filters

Transformer-Based Wavelet-Scalogram Deep Learning for Improved Seizure Pattern Recognition in Post-Hypoxic-Ischemic Fetal Sheep EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hypoxic-ischemic (HI) events in newborns can trigger seizures, which are highly associated with later neurodevelopmental impairment. The precise detection of these seizures is a complex task requiring considerable very specialized expertise, undersco...

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Developments in neural networks and sensing technologies have increased focus on modules for surface electromyogram (sEMG)-based pattern recognition. Incremental updating of parameters based on pre-trained networks can flexibly respond to user requir...

Human-in-the-Loop Myoelectric Pattern Recognition Control of an Arm-Support Robot to Improve Reaching in Stroke Survivors.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The objective of this study was to assess the feasibility and efficacy of using real-time human-in-the-loop pattern recognition-based myoelectric control to control vertical support force or vertical position to improve reach in individuals with chro...

Gesture Recognition Achieved by Utilizing LoRa Signals and Deep Learning.

Sensors (Basel, Switzerland)
This study proposes a novel gesture recognition system based on LoRa technology, integrating advanced signal preprocessing, adaptive segmentation algorithms, and an improved SS-ResNet50 deep learning model. Through the combination of residual learnin...

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.

JMIR formative research
BACKGROUND: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant...

Skeleton Reconstruction Using Generative Adversarial Networks for Human Activity Recognition Under Occlusion.

Sensors (Basel, Switzerland)
Recognizing human activities from motion data is a complex task in computer vision, involving the recognition of human behaviors from sequences of 3D motion data. These activities encompass successive body part movements, interactions with objects, o...

Novelty Recognition: Fish Species Classification via Open-Set Recognition.

Sensors (Basel, Switzerland)
To support the sustainable use of marine resources, regulations have been proposed to reduce fish discards focusing on the registration of all listed species. To comply with such regulations, computer vision methods have been developed. Nevertheless,...

Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms.

Neural networks : the official journal of the International Neural Network Society
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitations in achieving this objecti...

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

Neural networks : the official journal of the International Neural Network Society
Distributed machine learning in mobile adhoc networks faces significant challenges due to the limited computational resources of devices, non-IID data distribution, and dynamic network topology. Existing approaches often rely on centralized coordinat...

DRTN: Dual Relation Transformer Network with feature erasure and contrastive learning for multi-label image classification.

Neural networks : the official journal of the International Neural Network Society
The objective of multi-label image classification (MLIC) task is to simultaneously identify multiple objects present in an image. Several researchers directly flatten 2D feature maps into 1D grid feature sequences, and utilize Transformer encoder to ...