AIMC Topic: Deep Learning

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Efficient Epileptic Seizure Prediction Based on Deep Learning.

IEEE transactions on biomedical circuits and systems
Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning...

Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data.

Journal of proteome research
Deep learning (DL), a type of machine learning approach, is a powerful tool for analyzing large sets of data that are derived from biomedical sciences. However, it remains unknown whether DL is suitable for identifying contributing factors, such as b...

Depression recognition using machine learning methods with different feature generation strategies.

Artificial intelligence in medicine
The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method...

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data.

Scientific reports
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machin...

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.

BMC medical informatics and decision making
BACKGROUND: With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and s...

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

Magnetic resonance imaging
PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens thro...

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.

Mathematical biosciences
A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play signif...

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Physics in medicine and biology
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-onl...