AIMC Topic: Databases, Factual

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Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.

Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal of biomedical informatics
AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical softwa...

A deep convolutional neural network for the estimation of gas chromatographic retention indices.

Journal of chromatography. A
A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking ...

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...

A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.

Medical & biological engineering & computing
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict n...

His-GAN: A histogram-based GAN model to improve data generation quality.

Neural networks : the official journal of the International Neural Network Society
Generative Adversarial Network (GAN) has become an active research field due to its capability to generate quality simulation data. However, two consistent distributions (generated data distribution and original data distribution) produced by GAN can...

A new machine learning technique for an accurate diagnosis of coronary artery disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we de...

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

IEEE transactions on medical imaging
We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highl...

A Multilayer-Learning Current-Mode Neuromorphic System With Analog-Error Compensation.

IEEE transactions on biomedical circuits and systems
Internet-of-things applications that use machine-learning algorithms have increased the demand for application-specific energy-efficient hardware that can perform both learning and inference tasks to adapt to endpoint users or environmental changes. ...

Chemical-induced disease relation extraction via attention-based distant supervision.

BMC bioinformatics
BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedic...