AIMC Topic: Algorithms

Clear Filters Showing 5091 to 5100 of 28713 articles

Clinical Pilot of a Deep Learning Elastic Registration Algorithm to Improve Misregistration Artifact and Image Quality on Routine Oncologic PET/CT.

Academic radiology
RATIONALE AND OBJECTIVES: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve...

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy.

Computer methods and programs in biomedicine
This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to gener...

Highly valued subgoal generation for efficient goal-conditioned reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Goal-conditioned reinforcement learning is widely used in robot control, manipulating the robot to accomplish specific tasks by maximizing accumulated rewards. However, the useful reward signal is only received when the desired goal is reached, leadi...

Synchronization of time-delay dynamical networks via hybrid delayed impulses.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the synchronization problem of time-delay dynamical networks by means of hybrid delayed impulses, where synchronizing impulses and desynchronizing impulses can occur simultaneously. Some sufficient synchronization conditions a...

Using the super-learner to predict the chemical acute toxicity on rats.

Journal of hazardous materials
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...

CK-ATTnet: Medical image segmentation network based on convolutional kernel attention.

Computers in biology and medicine
The medical image partition model has a wide range of application prospects in medical diagnosis and treatment and has become an important auxiliary method to improve the diagnostic level by medical imaging analysis. After the feature extraction abil...

LTMSegnet: Lightweight multi-scale medical image segmentation combining Transformer and MLP.

Computers in biology and medicine
Medical image segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. In recent years, neural network-based methods have improved in terms of segmentation accuracy and become the mainstream in the field ...

GraphPBSP: Protein binding site prediction based on Graph Attention Network and pre-trained model ProstT5.

International journal of biological macromolecules
Protein-protein/peptide interactions play crucial roles in various biological processes. Exploring their interactions attracts wide attention. However, accurately predicting their binding sites remains a challenging task. Here, we develop an effectiv...

Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

Theranostics
Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD....

An interpretable deep learning model for detecting pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.

PeerJ
BACKGROUND: Determining the status of breast cancer susceptibility genes () is crucial for guiding breast cancer treatment. Nevertheless, the need for genetic testing among breast cancer patients remains unmet due to high costs and limited resources...