AIMC Topic: Deep Learning

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Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

The Canadian journal of cardiology
BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detect...

Improving blood cells classification in peripheral blood smears using enhanced incremental training.

Computers in biology and medicine
Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by medical specialists to assess some health aspects of individuals. The automation of blood analysis has attracted the attention of researchers in recent years, as it will not...

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives.

Analytical chemistry
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics...

Reconstruction of Organ Boundaries With Deep Learning in the D-Bar Method for Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to deter...

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

PLoS computational biology
N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing ...

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced read...

A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography.

The international journal of cardiovascular imaging
Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). ...

Adaptive optics for structured illumination microscopy based on deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from opt...

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

Medical image analysis
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...