AIMC Topic: Neural Networks, Computer

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STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation.

Biomedizinische Technik. Biomedical engineering
Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these s...

Emergence of integrated behaviors through direct optimization for homeostasis.

Neural networks : the official journal of the International Neural Network Society
Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeos...

Living cells and biological mechanisms as prototypes for developing chemical artificial intelligence.

Biochemical and biophysical research communications
Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More rec...

DeforT: Deformable transformer for visual tracking.

Neural networks : the official journal of the International Neural Network Society
Most trackers formulate visual tracking as common classification and regression (i.e., bounding box regression) tasks. Correlation features that are computed through depth-wise convolution or channel-wise multiplication operations are input into both...

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

The Pediatric infectious disease journal
BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sep...

Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols.

International journal of neural systems
The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membra...

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.

Medical image analysis
Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor...

Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter.

International journal of computer assisted radiology and surgery
PURPOSE: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves compe...

ConvMedSegNet: A multi-receptive field depthwise convolutional neural network for medical image segmentation.

Computers in biology and medicine
In order to achieve highly precise medical image segmentation, this paper presents ConvMedSegNet, a novel convolutional neural network designed with a U-shaped architecture that seamlessly integrates two crucial modules: the multi-receptive field dep...

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks.

Computers in biology and medicine
Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained signific...