AIMC Topic: Deep Learning

Clear Filters Showing 691 to 700 of 28423 articles

Construction of NH-MIL-125@ZnInS Z-Scheme Heterojunctions for Deep-Learning Assisted Photoelectrochemical Sensing of Dopamine.

Langmuir : the ACS journal of surfaces and colloids
Dopamine (DA) plays a pivotal role in modulating various physiological systems. Therefore, the ultrasensitive detection of DA holds substantial importance for the diagnosis and treatment of neurological disorders. Herein, a deep-learning-assisted sma...

Robust myocardium detection and scar severity classification in LGE-CMR using ScarYOLO and contrastive learning.

European journal of medical research
Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging plays a crucial role in assessing myocardial scar tissues, aiding in the diagnosis and prognosis of cardiovascular diseases. However, accurately classifying scar tissue severity...

Artificial intelligence-assisted endoscopic diagnosis system for diagnosing Helicobacter pylori infection: a multicenter study.

BMC medicine
BACKGROUND: Deep learning algorithm-based artificial intelligence (AI) has significantly advanced the domain of endoscopic diagnosis; however, its utilization for detecting Helicobacter pylori (H. pylori) infections remains constrained. We aimed to d...

VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.

Scientific reports
Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy-using more than one drug for treatment-can enhance ...

TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.

Scientific reports
Breast cancer continues to be a global public health challenge. An early and precise diagnosis is crucial for improving prognosis and efficacy. While deep learning (DL) methods have shown promising advances in breast cancer classification from mammog...

ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.

Scientific reports
Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive...

Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts.

Scientific reports
The rising prevalence of mental health disorders such as depression, anxiety, and bipolar disorder underscores the urgent need for effective tools to enable early detection and intervention. Social media platforms like Reddit offer a rich source of u...

Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion.

Scientific reports
Colorectal cancer (CRC) poses a significant global health burden, where early and accurate diagnosis is vital to improving patient outcomes. However, the structural complexity of CRC histopathological images renders manual analysis time-consuming and...

A hybrid approach for enhancing pseudo-labeling in medical images through pseudo-label refinement.

Scientific reports
Segmentation of medical images is critical for the evaluation, diagnosis, and treatment of various medical conditions. While deep learning-based approaches are the dominant methodology, they rely heavily on abundant labeled data and face significant ...