AIMC Topic: Deep Learning

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Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.

Nature communications
Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here w...

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

IEEE journal of biomedical and health informatics
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the...

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Nature biomedical engineering
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be di...

Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.

Journal of biomedical informatics
Electronic health records (EHRs) contain critical information useful for clinical studies. Early assessment of patients' mortality in intensive care units is of great importance. In this paper, a Deep Rule-Based Fuzzy System (DRBFS) was proposed to d...

Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders.

Journal of theoretical biology
In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the recombination is below average are consequently known as coldspots. Knowledge of...

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suf...

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

Journal of neural engineering
OBJECTIVE: Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG ...

Predicting sex from brain rhythms with deep learning.

Scientific reports
We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar...

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration.

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.

IEEE transactions on medical imaging
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a ne...