AIMC Topic: Neural Networks, Computer

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BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

Neural networks : the official journal of the International Neural Network Society
In recent years, Graph Neural Networks (GNNs) have garnered significant attention, with a notable focus on Graph Structure Learning (GSL), a branch dedicated to optimizing graph structures to enhance network training performance. Current GSL methods ...

A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.

Neural networks : the official journal of the International Neural Network Society
Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and s...

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI.

Neural networks : the official journal of the International Neural Network Society
Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificia...

Disentangled contrastive learning for fair graph representations.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-structured data through message passing, but their predictions are often correlated with sensitive attributes and thus lead to potential discrimination...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Medical image analysis
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haemat...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated se...

HIV-1 M group subtype classification using deep learning approach.

Computers in biology and medicine
Traditionally, the classification of HIV-1 M group subtypes has depended on statistical methods constrained by sample sizes. Here HIV-1-M-SPBEnv was proposed as the first deep learning-based method for classifying HIV-1 M group subtypes via env gene ...

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is p...

A unified multimodal classification framework based on deep metric learning.

Neural networks : the official journal of the International Neural Network Society
Multimodal classification algorithms play an essential role in multimodal machine learning, aiming to categorize distinct data points by analyzing data characteristics from multiple modalities. Extensive research has been conducted on distilling mult...