AIMC Topic: Deep Learning

Clear Filters Showing 9331 to 9340 of 28423 articles

Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology
BACKGROUND: The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasoun...

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

Spine
STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL).

Application and integration of deep learning in FAIMS for identifying acetone concentration.

Analytical biochemistry
In practical applications, analytical instruments are used for both qualitative and quantitative analysis. However, for high-field asymmetric-waveform ion mobility spectrometry (FAIMS), most studies to date have been focused on the qualitative analys...

De novo design of high-affinity binders of bioactive helical peptides.

Nature
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...

Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning.

Sensors (Basel, Switzerland)
Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognit...

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Genome biology
Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) da...

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

BMC genomic data
BACKGROUND: Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for ...

Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning.

Scientific reports
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of ...

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality allows clinicians to correctly identify diseases and treat patients accordingly. Here we aim to dev...