AIMC Topic: Deep Learning

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Forging Online Community Among People in Recovery From Substance Use: Natural Language Processing and Deep-Learning Analysis of The Phoenix App User-Generated Data.

JMIR mHealth and uHealth
BACKGROUND: Mobile apps are powerful tools for promoting and sustaining healthy behaviors, including supporting diverse recovery pathways from substance use, including alcohol use disorder. Indeed, prior research strongly supports the notion that soc...

A teacherless lightweight classification framework for benign and malignant pulmonary nodules based on GAS.

Biomedical physics & engineering express
Deep learning methods have been widely adopted for classifying benign and malignant pulmonary nodules. However, existing models often suffer from high memory usage, computational cost, and large parameter counts. As a result, the development of light...

SynSeg: A synthetic data-driven approach for robust subcellular structure segmentation.

The Journal of cell biology
Accurate subcellular segmentation is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manual...

BGC-MAC and BGC-MAP: Attention-Based Models for Biosynthetic Gene Cluster Classification and Product Matching.

Journal of chemical information and modeling
Natural products, synthesized via enzymes encoded by biosynthetic gene clusters (BGCs), represent a major source of therapeutic agents. Accurate BGC annotation is essential to unlocking the vast potential of natural product diversity. However, BGC an...

Learning feature dependencies for precise tumor region detection and segmentation in optical coherence tomography images.

International ophthalmology
PURPOSE: Accurate segmentation of tumor-infected regions in retinal Optical Coherence Tomography (OCT) images is critical for early diagnosis and clinical decision-making. However, conventional deep learning and transformer-based models often struggl...

Dual-channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.

Biomedical physics & engineering express
. To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRC...

Incorporating multi-modal prompt learning into foundation models enhances predictability of visual fMRI responses to dynamic natural stimuli.

Journal of neural engineering
. Modeling neural encoding of visual stimuli often uses deep neural networks (DNNs) to predict human brain response to external stimuli. However, each DNN depends on networks tailored for computer vision tasks, resulting in suboptimal brain correspon...

Regional-aware and sequence-informed multi-decoder network for robust brain glioma segmentation in multi-parametric MRI.

Computers in biology and medicine
Accurate segmentation of glioblastoma subregions from multi-parametric MRI is essential for diagnosis, surgical planning, and treatment monitoring in neuro-oncology. However, effective delineation of surrounding non-enhancing FLAIR hyperintensity, no...

Deep Learning-Assisted G4 Nanowire-Enhanced Carbon Dot Biosensor for Exosomal LncRNA Artificial Intelligence Diagnosis.

Analytical chemistry
Exosomal long noncoding RNAs (lncRNA) have significant potential as a biomarker for early cancer diagnosis. Accurate and sensitive detection of this abnormal expression remains challenging. Herein, we develop an innovative dual-mode photoelectrochemi...

Deep Learning Algorithms Enabled Visual Detection of Anthrax Biomarkers by MnO Nanozyme-Based Colorimetric Sensor Array.

Analytical chemistry
This study develops an innovative approach that integrates a colorimetric sensor array (CSA) composed of phenylalanine-modified MnO nanozymes with advanced algorithms, aiming to detect the anthrax biomarker 2,6-pyridine dicarboxylic acid (2,6-PDA) an...