AIMC Topic: Algorithms

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EmT: A Novel Transformer for Generalized Cross-Subject EEG Emotion Recognition.

IEEE transactions on neural networks and learning systems
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been a limited emphasis on ...

Neuroadaptive Admittance Control for Human-Robot Interaction With Human Motion Intention Estimation and Output Error Constraint.

IEEE transactions on cybernetics
Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. ...

Deep-learning-assisted medium optimization improves hyaluronic acid production by Streptococcus zooepidemicus.

Journal of bioscience and bioengineering
To improve the efficiency of hyaluronic acid production by Streptococcus zooepidemicus, the growth medium was optimized with a pipeline involving a deep learning (DL) algorithm. To train the DL model, the initial training dataset (OA01-18) was design...

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large curated multi-modality datasets with the promising performance of GAN...

S-Net: A novel shallow network for enhanced detail retention in medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, deep U-shaped network architectures have been widely applied to medical image segmentation tasks, achieving notable successes. However, the inherent limitation of this architecture is that multiple down-samp...

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...

Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis ...

Self-supervised multi-modality learning for multi-label skin lesion classification.

Computer methods and programs in biomedicine
BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide detailed views of surface structures, while clinical images offer complementary macroscopic information. Clini...

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.

International journal of methods in psychiatric research
BACKGROUND: Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects.