AIMC Topic: Deep Learning
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A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study.
BMC medicine
May 7, 2025
BACKGROUND: CT images and circulating tumor cells (CTCs) are indispensable for diagnosing the mediastinal lesions by providing radiological and intra-tumoral information. This study aimed to develop and validate a deep multimodal fusion network (DMFN...
Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.
Scientific reports
May 7, 2025
This study aims to deepen the understanding and classification of tinnitus through a comprehensive analysis of EEG signals utilizing innovative microstate analysis techniques and cutting-edge machine learning approaches. EEG data were collected from ...
Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.
Scientific reports
May 7, 2025
The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unprecedented accuracy in analyzing complex medical images. Deep learning-based segmentation holds signi...
EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.
Scientific reports
May 7, 2025
In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and t...
Single-microphone deep envelope separation based auditory attention decoding for competing speech and music.
Journal of neural engineering
May 7, 2025
In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a competing speech and music setup. Deep source separation is applied directly on the envelope of the obse...
Speckle pattern analysis with deep learning for low-cost stroke detection: a phantom-based feasibility study.
Journal of biomedical optics
May 7, 2025
SIGNIFICANCE: Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. However, many advanced imaging modalities (computerized tomography, magnetic resonance imaging) remain ina...
Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data.
PLoS computational biology
May 7, 2025
We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional externa...
Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
May 7, 2025
Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capab...
OA-HybridCNN (OHC): An advanced deep learning fusion model for enhanced diagnostic accuracy in knee osteoarthritis imaging.
PloS one
May 7, 2025
Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset ...
A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.
PloS one
May 7, 2025
BACKGROUND: Predicting protein-DNA binding sites in vivo is a challenging but urgent task in many fields such as drug design and development. Most promoters contain many transcription factor (TF) binding sites, yet only a few have been identified thr...
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