AIMC Topic: Deep Learning

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A human-computer collaboration for COVID-19 differentiation: combining a radiomics model with deep learning and human auditing.

Annals of palliative medicine
BACKGROUND: This study aimed to build a radiomics model with deep learning (DL) and human auditing and examine its diagnostic value in differentiating between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP).

The predictive skill of convolutional neural networks models for disease forecasting.

PloS one
In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenz...

Automatic detection of papilledema through fundus retinal images using deep learning.

Microscopy research and technique
Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities co...

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

The Science of the total environment
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, p...

MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility dis...

Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: data from the Multicenter Osteoarthritis Study (MOST).

Osteoarthritis and cartilage
OBJECTIVE: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

Deep learning for peptide identification from metaproteomics datasets.

Journal of proteomics
Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled wi...

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images ...

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.

Journal of proteome research
Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to pre...