AIMC Topic: Deep Learning

Clear Filters Showing 24111 to 24120 of 28423 articles

Molecular landscape of endometrioid Cancer: Integrating multiomics and deep learning for personalized survival prediction.

Computers in biology and medicine
BACKGROUND: The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.

Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND AND PURPOSE: The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thor...

Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data.

NeuroImage
OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often ...

Deep learning meets marine biology: Optimized fused features and LIME-driven insights for automated plankton classification.

Computers in biology and medicine
Plankton are microorganisms that play an important role in marine food webs as primary producers in the trophic web. Traditional plankton identification methods using manual microscopy and sampling are time-consuming, labor-intensive, and prone to er...

An explainable adaptive channel weighting-based deep convolutional neural network for classifying renal disorders in computed tomography images.

Computers in biology and medicine
Renal disorders are a significant public health concern and a cause of mortality related to renal failure. Manual diagnosis is subjective, labor-intensive, and depends on the expertise of nephrologists in renal anatomy. To improve workflow efficiency...

DeepValve: The first automatic detection pipeline for the mitral valve in Cardiac Magnetic Resonance imaging.

Computers in biology and medicine
Mitral valve (MV) assessment is key to diagnosing valvular disease and to addressing its serious downstream complications. Cardiac magnetic resonance (CMR) has become an essential diagnostic tool in MV disease, offering detailed views of the valve st...

Cancer type and survival prediction based on transcriptomic feature map.

Computers in biology and medicine
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

Computers in biology and medicine
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.

Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face chall...

Comparative analysis of deep learning models for predicting biocompatibility in tissue scaffold images.

Computers in biology and medicine
MOTIVATION: Bioprinting enables the creation of complex tissue scaffolds, which are vital for tissue engineering. However, predicting scaffold biocompatibility before fabrication remains a critical challenge, potentially leading to inefficiencies and...