AIMC Topic: Deep Learning
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The analysis of optimization in music aesthetic education under artificial intelligence.
Scientific reports
Apr 4, 2025
In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discu...
Deep learning prediction of mammographic breast density using screening data.
Scientific reports
Apr 4, 2025
This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fatty, scattered, heterogeneously dense, and extremely dense). A retrospective analysis was conducted ...
A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.
Scientific reports
Apr 4, 2025
Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove...
Advancing plant leaf disease detection integrating machine learning and deep learning.
Scientific reports
Apr 4, 2025
Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the diffic...
MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.
Science advances
Apr 4, 2025
Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and ...
Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.
Oncotarget
Apr 4, 2025
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in he...
Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery.
Environment international
Apr 4, 2025
BACKGROUND: Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-based vegetation indices, leading to potential exposure misclassification and limited policy relevance. ...
Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...
SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration.
IEEE journal of biomedical and health informatics
Apr 4, 2025
It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based net...
Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning technique...
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