AIMC Topic: Neural Networks, Computer

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Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.

Scientific reports
Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to ad...

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases.

Scientific reports
The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for ...

Image biomarkers and explainable AI: handcrafted features versus deep learned features.

European radiology experimental
Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components o...

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

Journal of biomedical optics
SIGNIFICANCE: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair presence in dermoscopic images can obscure important features, complicating the diagnostic process....

Ag@CDS SERS substrate coupled with lineshape correction algorithm and BP neural network to detect thiram in beverages.

Talanta
Surface enhanced Raman scattering (SERS) has been proved an effective analytical technique due to its high sensitivity, however, how to identify and extract useful information from raw SERS spectra is still a problem that needs to be resolved. In thi...

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Anomaly Detection (FSAD) in industrial images aims to identify abnormalities using only a few normal images, which is crucial for industrial scenarios where sample training is limited. The recent advances in large-scale pre-trained visual-la...

Diminishing spectral bias in physics-informed neural networks using spatially-adaptive Fourier feature encoding.

Neural networks : the official journal of the International Neural Network Society
Physics-informed neural networks (PINNs) have recently emerged as a promising framework for solving partial differential equation (PDE) systems in computer mechanics. However, PINNs still struggle in simulating systems whose solution functions exhibi...

MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging.

Medical physics
BACKGROUND: Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.

High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.

Environmental pollution (Barking, Essex : 1987)
Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional f...

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Journal of chemical information and modeling
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essenti...