AIMC Topic: Deep Learning

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EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

Computer methods and programs in biomedicine Feb 8, 2025
BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are ...
Alzheimer Disease Frontotemporal Dementia Electroencephalography Signal Processing, Computer-Assisted Algorithms Deep Learning Convolutional Neural Networks Humans Neural Networks, Computer
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Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Feb 8, 2025
PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.
Radiotherapy Planning, Computer-Assisted Male Middle Aged Head and Neck Neoplasms Magnetic Resonance Imaging Deep Learning Tomography, X-Ray Computed Female Aged Humans
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Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology Feb 8, 2025
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2...
Molecular Dynamics Simulation Protein Conformation Artificial Intelligence Proteins Deep Learning Drug Discovery Models, Molecular
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Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Feb 8, 2025
Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP ...
Animals Candidiasis Peptides Microbial Sensitivity Tests Deep Learning Antifungal Agents Drug Design Candida albicans Quantitative Structure-Activity Relationship
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ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Medical engineering & physics Feb 8, 2025
Tuberculosis is a high-mortality infectious disease. Manual sputum smear microscopy is a common and effective method for screening tuberculosis. However, it is time-consuming, labor-intensive, and has low sensitivity. In this study, we propose ResGlo...
Tuberculosis Image Processing, Computer-Assisted Deep Learning Microscopy Humans Sputum Neural Networks, Computer
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DeepReducer: A linear transformer-based model for MEG denoising.

NeuroImage Feb 8, 2025
Measuring event-related magnetic fields (ERFs) in magnetoencephalography (MEG) is crucial for investigating perceptual and cognitive information processing in both neuroscience research and clinical practice. However, the magnitude of the ERF in cort...
Brain Deep Learning Artifacts Signal-To-Noise Ratio Humans Magnetoencephalography Signal Processing, Computer-Assisted
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HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.

Methods (San Diego, Calif.) Feb 8, 2025
Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gai...
Promoter Regions, Genetic Mice Computational Biology Deep Learning Neural Networks, Computer Humans Escherichia coli Software Sequence Analysis, DNA Animals DNA
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Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Computers in biology and medicine Feb 8, 2025
Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. However, existing approaches such as polysomnography, considered the most reliable and accurate test to de...
Algorithms Databases, Factual Female Humans Signal Processing, Computer-Assisted Sleep Apnea Syndromes Polysomnography Deep Learning Electrocardiography Male
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Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning.

Computers in biology and medicine Feb 8, 2025
Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manua...
Humans Lung Tomography, X-Ray Computed Deep Learning Imaging, Three-Dimensional
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A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.

Current oncology (Toronto, Ont.) Feb 8, 2025
BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management.
Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Algorithms Female Humans Tomography, X-Ray Computed Deep Learning
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