AIMC Topic: Algorithms

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SH: Long-tailed classification via spatial constraint sampling, scalable network, and hybrid task.

Neural networks : the official journal of the International Neural Network Society
Long-tailed classification is a significant yet challenging vision task that aims to making the clearest decision boundaries via integrating semantic consistency and texture characteristics. Unlike prior methods, we design spatial constraint sampling...

Incremental model-based reinforcement learning with model constraint.

Neural networks : the official journal of the International Neural Network Society
In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by b...

Virtual torque control combining with modal decoupling research for hydraulic-driven lower limb exoskeleton robot.

ISA transactions
The hydraulic-driven lower limb exoskeleton robot (HDLLER) can provide excellent assistance during human walking. However, complex torque coupling disturbances exist between each joint, negatively impacting the precise torque tracking of each joint c...

Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical parcellation.

Medical image analysis
Cortical parcellation delineates the cerebral cortex into distinct regions according to their distinctiveness in anatomy and/or function, which is a fundamental preprocess in brain cortex analysis and can influence the accuracy and specificity of sub...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are ...

Learning robust medical image segmentation from multi-source annotations.

Medical image analysis
Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. However, learning segmentation networks from multi-sourc...

Unveiling NLR pathway signatures: EP300 and CPN60 markers integrated with clinical data and machine learning for precision NASH diagnosis.

Cytokine
BACKGROUND: Given the increasing prevalence of metabolic dysfunction-associated fatty liver disease (MAFLD) and non-alcoholic steatohepatitis (NASH), there is a critical need for accurate non-invasive early diagnostic markers.

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Computers in biology and medicine
Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. However, existing approaches such as polysomnography, considered the most reliable and accurate test to de...

A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.

Current oncology (Toronto, Ont.)
BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management.

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Scientific reports
Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalitie...