AIMC Topic: Deep Learning

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Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis.

Medical image analysis
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical pr...

Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this ...

Deep Learning in Microscopy Image Analysis: A Survey.

IEEE transactions on neural networks and learning systems
Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a l...

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Analytical chemistry
In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides ap...

Classification of Medical Images in the Biomedical Literature by Jointly Using Deep and Handcrafted Visual Features.

IEEE journal of biomedical and health informatics
The classification of medical images and illustrations from the biomedical literature is important for automated literature review, retrieval, and mining. Although deep learning is effective for large-scale image classification, it may not be the opt...

White blood cells identification system based on convolutional deep neural learning networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear im...

A happiness degree predictor using the conceptual data structure for deep learning architectures.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventio...

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Scientific reports
Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic image...

Transductive Ridge Regression in Structure-activity Modeling.

Molecular informatics
In this article we consider the application of the Transductive Ridge Regression (TRR) approach to structure-activity modeling. An original procedure of the TRR parameters optimization is suggested. Calculations performed on 3 different datasets invo...