AIMC Topic: Deep Learning

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Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations.

Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%.

A new age in protein design empowered by deep learning.

Cell systems
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for milli...

Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.

Journal of the American College of Cardiology
BACKGROUND: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function.

Severe aortic stenosis detection by deep learning applied to echocardiography.

European heart journal
BACKGROUND AND AIMS: Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that r...

Fusang: a framework for phylogenetic tree inference via deep learning.

Nucleic acids research
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Nucleic acids research
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to deter...

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images.

Medicine
Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography ima...