AIMC Topic: Deep Learning

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DKCN-Net: Deep kronecker convolutional neural network-based lung disease detection with federated learning.

Computational biology and chemistry
In the healthcare field, lung disease detection techniques based on deep learning (DL) are widely used. However, achieving high stability while maintaining privacy remains a challenge. To address this, this research employs Federated Learning (FL), e...

PrediRep: Modeling hierarchical predictive coding with an unsupervised deep learning network.

Neural networks : the official journal of the International Neural Network Society
Hierarchical predictive coding (hPC) provides a compelling framework for understanding how the cortex predicts future sensory inputs by minimizing prediction errors through an internal generative model of the external world. Existing deep learning mo...

Geometric deep learning with adaptive full-band spatial diffusion for accurate, efficient, and robust cortical parcellation.

Medical image analysis
Cortical parcellation delineates the cerebral cortex into distinct regions according to their distinctiveness in anatomy and/or function, which is a fundamental preprocess in brain cortex analysis and can influence the accuracy and specificity of sub...

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
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 ...

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
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.

Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology
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...

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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 ...

ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Medical engineering & physics
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...

DeepReducer: A linear transformer-based model for MEG denoising.

NeuroImage
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...

HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.

Methods (San Diego, Calif.)
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...