AIMC Topic: Deep Learning

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Spatial resolution improved fluorescence lifetime imaging via deep learning.

Optics express
We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural net...

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for descri...

SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and an early-stage component of typical protein 3D structure prediction pipelines.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computat...

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

Mathematical biosciences and engineering : MBE
The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid te...

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time con...

3D k-space reflectance fluorescence tomography via deep learning.

Optics letters
We report on the potential to perform image reconstruction in 3D k-space reflectance fluorescence tomography (FT) using deep learning (DL). Herein, we adopt a modified AUTOMAP architecture and develop a training methodology that leverages an open-sou...

Deep learning-enhanced, open-source eigenmode expansion.

Optics letters
We present an open-source eigenmode expansion (EME) software package entirely implemented in the Python programming language. Eigenmode expansion Python (EMEPy) utilizes artificial neural networks to reproduce electromagnetic eigenmode field profiles...

Deep learning Mueller matrix feature retrieval from a snapshot Stokes image.

Optics express
A Mueller matrix (MM) provides a comprehensive representation of the polarization properties of a complex medium and encodes very rich information on the macro- and microstructural features. Histopathological features can be characterized by polariza...

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.

Briefings in bioinformatics
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are cost...