AIMC Topic: Deep Learning

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VDE-Net: a two-stage deep learning method for phase unwrapping.

Optics express
Phase unwrapping is a critical step to obtaining a continuous phase distribution in optical phase measurements and coherent imaging techniques. Traditional phase-unwrapping methods are generally low performance due to significant noise or undersampli...

Enhanced Diagnosis of Plaque Erosion by Deep Learning in Patients With Acute Coronary Syndromes.

JACC. Cardiovascular interventions
BACKGROUND: Acute coronary syndromes caused by plaque erosion might be potentially managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires expertise in optical coherence tomographic (OCT) image interpretation. In ...

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

Medicine
BACKGROUND: To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspect...

Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease.

Medicine
BACKGROUND: Several recent studies have reported that deep learning reconstruction "TrueFidelity" (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in p...

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Nucleic acids research
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification...

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of h...

pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

Bioinformatics (Oxford, England)
SUMMARY: Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption ...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and funct...