AIMC Topic: Deep Learning

Clear Filters Showing 4681 to 4690 of 28423 articles

Assessing the effects of 5-HT and 5-HT receptor antagonists on DOI-induced head-twitch response in male rats using marker-less deep learning algorithms.

Pharmacological reports : PR
BACKGROUND: Serotonergic psychedelics, which display a high affinity and specificity for 5-HT receptors like 2,5-dimethoxy-4-iodoamphetamine (DOI), reliably induce a head-twitch response in rodents characterized by paroxysmal, high-frequency head rot...

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.

Journal of X-ray science and technology
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...

Clinical feasibility of a deep learning approach for conventional and synthetic diffusion-weighted imaging in breast cancer: Qualitative and quantitative analyses.

European journal of radiology
PURPOSE: In this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstr...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Computers in biology and medicine
Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build...

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping.

Computers in biology and medicine
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base...

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification.

IEEE transactions on cybernetics
Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) ca...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Sensors (Basel, Switzerland)
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnost...

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning.

Sensors (Basel, Switzerland)
The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for m...

Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset.

Sensors (Basel, Switzerland)
The increasing number of individuals with disabilities-over 61 million adults in the United States alone-underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and of...