AIMC Topic: Deep Learning

Clear Filters Showing 26021 to 26030 of 28423 articles

Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.

Translational vision science & technology
PURPOSE: The structural changes measured by optical coherence tomography (OCT) are related to functional changes in visual fields (VFs). This study aims to accurately assess the structure-function relationship and overcome the challenges brought by t...

Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.

Translational vision science & technology
PURPOSE: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning.

Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms.

Radiology
Background Handcrafted radiomics and deep learning (DL) models individually achieve good performance in lesion classification (benign vs malignant) on contrast-enhanced mammography (CEM) images. Purpose To develop a comprehensive machine learning too...

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

Bioinformatics (Oxford, England)
MOTIVATION: Interpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways ha...

A deep learning algorithm-based visual strategy intervention study for children with autism spectrum disorders - extraction and detection of children's behavioral features.

European review for medical and pharmacological sciences
OBJECTIVE: Autism spectrum disorder is a group of neurodevelopmental disorders. The disease's etiology is unclear, and there is no specific drug treatment for the core symptoms of autism spectrum disease. The study aims to explore effective intervent...

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Bioinformatics (Oxford, England)
MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in th...