AIMC Topic: Deep Learning

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Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract.

Journal of cataract and refractive surgery
PURPOSE: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.

AUTOMATED DETECTION OF VITRITIS USING ULTRAWIDE-FIELD FUNDUS PHOTOGRAPHS AND DEEP LEARNING.

Retina (Philadelphia, Pa.)
BACKGROUND/PURPOSE: Evaluate the performance of a deep learning algorithm for the automated detection and grading of vitritis on ultrawide-field imaging.

Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance.

European heart journal. Cardiovascular Imaging
AIMS: This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular ...

Conversion of single-energy CT to parametric maps of dual-energy CT using convolutional neural network.

The British journal of radiology
OBJECTIVES: We propose a deep learning (DL) multitask learning framework using convolutional neural network for a direct conversion of single-energy CT (SECT) to 3 different parametric maps of dual-energy CT (DECT): virtual-monochromatic image (VMI),...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Systematic biology
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...

Classification of Motor Imagery Tasks Derived from Unilateral Upper Limb based on a Weight-optimized Learning Model.

Journal of integrative neuroscience
BACKGROUND: The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex.

Deep learning application to automated classification of recommendations made by hospital pharmacists during medication prescription review.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
PURPOSE: Recommendations to improve therapeutics are proposals made by pharmacists during the prescription review process to address suboptimal use of medicines. Recommendations are generated daily as text documents but are rarely reused beyond their...

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

Briefings in bioinformatics
Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their divers...

Discovering predisposing genes for hereditary breast cancer using deep learning.

Briefings in bioinformatics
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be iden...

PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.

Briefings in bioinformatics
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitatio...